{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a2f143c7",
   "metadata": {},
   "source": [
    "# 神经网络编程练习中\n",
    "\n",
    "本章我们会逐步完成：\n",
    "* 编码实现Dropout传播；\n",
    "* 编码组合Affine-ReLU-Dropout层；\n",
    "* 编码实现Dropout神经网络；\n",
    "* 解耦神经网络；\n",
    "* 正则化比较实验。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8ac29747",
   "metadata": {},
   "outputs": [],
   "source": [
    "# -*- coding: utf-8 -*-\n",
    "import time\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from classifiers.chapter4 import *\n",
    "from utils import *\n",
    "\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) \n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "def rel_error(x, y):\n",
    "    \"\"\" 返回相对误差 \"\"\"\n",
    "    return np.max(np.abs(x - y) / (np.maximum(1e-8, np.abs(x) + np.abs(y))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "29433c51",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train:  (49000, 3, 32, 32)\n",
      "y_train:  (49000,)\n",
      "X_val:  (1000, 3, 32, 32)\n",
      "y_val:  (1000,)\n",
      "X_test:  (1000, 3, 32, 32)\n",
      "y_test:  (1000,)\n"
     ]
    }
   ],
   "source": [
    "# 载入预处理后的数据\n",
    "data = get_CIFAR10_data()\n",
    "for k, v in data.items():\n",
    "    print('%s: ' % k, v.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b9a5dfa",
   "metadata": {},
   "source": [
    "## Dropout 前向传播\n",
    "\n",
    "由于Dropout的训练阶段和测试阶段采取不一样的传播方式，因此我们会设置“test”以及“train”模式。在训练模式时，我们会将单独的神经元激活概率P生成mask掩码层。该掩码层中为“0”的位置表明该位置处神经元处于抑制状态，为“1”的位置表明该处神经元可用。在测试阶段，我们去除掩码操作，直接返回输入结果即可。\n",
    "\n",
    "执行下面的代码，进行检验。测试概率p与训练阶段输出0的平均个数相加与1接近。\n",
    "\n",
    "Dropout使一部分神经元失活，那么该层神经元的输出均值就会缩小p倍。这在零均值的时候没有影响，但如果我们的神经元不是零均值分布的，那么可能就会收到影响。为了消除这种影响，我们使用mask=mask/p，这样就可以保证输出均值与输入均值相同，并且也间接放大了可用神经元的“影响力”。而现在神经元处于激活状态时，其输出值会被方法1/p倍。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "527b34ef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "测试概率 p =  0.3\n",
      "均值输入:  10.004673052448744\n",
      "训练阶段输出均值:  10.066262644654797\n",
      "测试阶段输出均值:  10.004673052448744\n",
      "训练阶段输出为0的平均个数:  0.698144\n",
      "测试阶段输出为0的平均个数:  0.0\n",
      "测试概率 p =  0.6\n",
      "均值输入:  10.004673052448744\n",
      "训练阶段输出均值:  10.00726350830372\n",
      "测试阶段输出均值:  10.004673052448744\n",
      "训练阶段输出为0的平均个数:  0.399956\n",
      "测试阶段输出为0的平均个数:  0.0\n",
      "测试概率 p =  0.75\n",
      "均值输入:  10.004673052448744\n",
      "训练阶段输出均值:  10.019386026673144\n",
      "测试阶段输出均值:  10.004673052448744\n",
      "训练阶段输出为0的平均个数:  0.248956\n",
      "测试阶段输出为0的平均个数:  0.0\n"
     ]
    }
   ],
   "source": [
    "from classifiers.chapter4.dropout_layers import *\n",
    "\n",
    "x = np.random.randn(500, 500) + 10\n",
    "\n",
    "for p in [0.3, 0.6, 0.75]:\n",
    "    out, _ = dropout_forward(x, {'mode': 'train', 'p': p})\n",
    "    out_test, _ = dropout_forward(x, {'mode': 'test', 'p': p})\n",
    "\n",
    "    print('测试概率 p = ', p)\n",
    "    print('均值输入: ', x.mean())\n",
    "    print('训练阶段输出均值: ', out.mean())\n",
    "    print('测试阶段输出均值: ', out_test.mean())\n",
    "    print('训练阶段输出为0的平均个数: ',(out == 0).mean())\n",
    "    print('测试阶段输出为0的平均个数: ',(out_test == 0).mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40687c89",
   "metadata": {},
   "source": [
    "## Dropout 反向传播\n",
    "\n",
    "反向传播同样分为测试和训练两种模式。在测试阶段，仅仅返回上层梯度即可。在训练阶段需要将处于抑制状态神经元所对应的上层梯度设置为0。因此需要使用mask，其已经放在cache中。\n",
    "\n",
    "当你完成编码工作后，执行下列代码块进行检验："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d3a215a0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dx 相对误差:  5.4456110075440367e-11\n"
     ]
    }
   ],
   "source": [
    "from utils import *\n",
    "\n",
    "x = np.random.randn(10, 10) + 10\n",
    "dout = np.random.randn( * x.shape )\n",
    "\n",
    "dropout_param = { 'mode': 'train', 'p': 0.8, 'seed': 123 }\n",
    "out, cache = dropout_forward( x, dropout_param )\n",
    "dx = dropout_backward( dout, cache )\n",
    "# 计算梯度\n",
    "dx_num = eval_numerical_gradient_array( lambda xx: \n",
    "                                       dropout_forward( xx, dropout_param)[0], x, dout)\n",
    "\n",
    "print('dx 相对误差: ', rel_error( dx, dx_num ))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f78fd16",
   "metadata": {},
   "source": [
    "## Dropout全连接神经网络\n",
    "\n",
    "接下来,我们将Dropout功能添加到深层全连接神经网络中。主要完成Dropout神经网络初始化以及损失函数的构建。\n",
    "\n",
    "然后进行梯度校验。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "18d393b6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "检验 dropout率 =  0\n",
      "初始化 loss:  2.3051948273987857\n",
      "W1 相对误差: 2.53e-07\n",
      "W2 相对误差: 1.50e-05\n",
      "W3 相对误差: 2.75e-07\n",
      "b1 相对误差: 2.94e-06\n",
      "b2 相对误差: 5.05e-08\n",
      "b3 相对误差: 1.17e-10\n",
      "\n",
      "检验 dropout率 =  0.2\n",
      "初始化 loss:  2.2821321641253425\n",
      "W1 相对误差: 7.28e-09\n",
      "W2 相对误差: 8.40e-10\n",
      "W3 相对误差: 5.63e-09\n",
      "b1 相对误差: 1.64e-09\n",
      "b2 相对误差: 2.21e-10\n",
      "b3 相对误差: 1.48e-10\n",
      "\n",
      "检验 dropout率 =  0.5\n",
      "初始化 loss:  2.3011143171656236\n",
      "W1 相对误差: 1.89e-07\n",
      "W2 相对误差: 1.94e-06\n",
      "W3 相对误差: 2.04e-08\n",
      "b1 相对误差: 5.02e-08\n",
      "b2 相对误差: 2.45e-09\n",
      "b3 相对误差: 1.66e-10\n",
      "\n",
      "检验 dropout率 =  0.7\n",
      "初始化 loss:  2.3040345536668467\n",
      "W1 相对误差: 1.77e-07\n",
      "W2 相对误差: 7.78e-07\n",
      "W3 相对误差: 3.39e-08\n",
      "b1 相对误差: 1.77e-08\n",
      "b2 相对误差: 3.20e-09\n",
      "b3 相对误差: 8.99e-11\n",
      "\n"
     ]
    }
   ],
   "source": [
    "N, D, H1, H2, C = 2, 15, 20, 30, 10\n",
    "X = np.random.randn(N, D)\n",
    "y = np.random.randint(C, size=(N,))\n",
    "\n",
    "for dropout in [0, 0.2, 0.5,0.7]:\n",
    "    print('检验 dropout率 = ', dropout)\n",
    "    model = FullyConnectedNet(input_dim=D,hidden_dims=[H1, H2],    num_classes=C,\n",
    "                                                        weight_scale=5e-2, dropout=dropout, seed=13)\n",
    "\n",
    "    loss, grads = model.loss(X, y)\n",
    "    print('初始化 loss: ', loss)\n",
    "\n",
    "    for name in sorted(grads):\n",
    "        f = lambda _: model.loss(X, y)[0]\n",
    "        grad_num = eval_numerical_gradient(f, model.params[name], verbose=False, h=1e-5)\n",
    "        print('%s 相对误差: %.2e' % (name, rel_error(grad_num, grads[name])))\n",
    "        \n",
    "    print()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d943970",
   "metadata": {},
   "source": [
    "# trainer\n",
    "\n",
    "解耦神经网络训练过程，打开`chapter4\\trainer.py`阅读相关内容"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2ce2b0e8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(迭代 1 / 4900) 损失值: 5.034513\n",
      "(周期 0 / 20) 训练精度: 0.129000; 验证精度: 0.152000\n",
      "(迭代 201 / 4900) 损失值: 1.934388\n",
      "(周期 1 / 20) 训练精度: 0.375000; 验证精度: 0.370000\n",
      "(迭代 401 / 4900) 损失值: 1.627177\n",
      "(周期 2 / 20) 训练精度: 0.459000; 验证精度: 0.435000\n",
      "(迭代 601 / 4900) 损失值: 1.569923\n",
      "(周期 3 / 20) 训练精度: 0.495000; 验证精度: 0.453000\n",
      "(迭代 801 / 4900) 损失值: 1.422986\n",
      "(周期 4 / 20) 训练精度: 0.501000; 验证精度: 0.457000\n",
      "(迭代 1001 / 4900) 损失值: 1.280008\n",
      "(迭代 1201 / 4900) 损失值: 1.443328\n",
      "(周期 5 / 20) 训练精度: 0.495000; 验证精度: 0.462000\n",
      "(迭代 1401 / 4900) 损失值: 1.373405\n",
      "(周期 6 / 20) 训练精度: 0.530000; 验证精度: 0.477000\n",
      "(迭代 1601 / 4900) 损失值: 1.353594\n",
      "(周期 7 / 20) 训练精度: 0.538000; 验证精度: 0.479000\n",
      "(迭代 1801 / 4900) 损失值: 1.359898\n",
      "(周期 8 / 20) 训练精度: 0.544000; 验证精度: 0.476000\n",
      "(迭代 2001 / 4900) 损失值: 1.328637\n",
      "(迭代 2201 / 4900) 损失值: 1.244217\n",
      "(周期 9 / 20) 训练精度: 0.551000; 验证精度: 0.494000\n",
      "(迭代 2401 / 4900) 损失值: 1.375081\n",
      "(周期 10 / 20) 训练精度: 0.575000; 验证精度: 0.486000\n",
      "(迭代 2601 / 4900) 损失值: 1.317961\n",
      "(周期 11 / 20) 训练精度: 0.569000; 验证精度: 0.480000\n",
      "(迭代 2801 / 4900) 损失值: 1.230606\n",
      "(周期 12 / 20) 训练精度: 0.543000; 验证精度: 0.489000\n",
      "(迭代 3001 / 4900) 损失值: 1.316827\n",
      "(周期 13 / 20) 训练精度: 0.587000; 验证精度: 0.517000\n",
      "(迭代 3201 / 4900) 损失值: 1.193300\n",
      "(迭代 3401 / 4900) 损失值: 1.097745\n",
      "(周期 14 / 20) 训练精度: 0.568000; 验证精度: 0.485000\n",
      "(迭代 3601 / 4900) 损失值: 0.996784\n",
      "(周期 15 / 20) 训练精度: 0.596000; 验证精度: 0.519000\n",
      "(迭代 3801 / 4900) 损失值: 1.125491\n",
      "(周期 16 / 20) 训练精度: 0.621000; 验证精度: 0.495000\n",
      "(迭代 4001 / 4900) 损失值: 1.122187\n",
      "(周期 17 / 20) 训练精度: 0.630000; 验证精度: 0.512000\n",
      "(迭代 4201 / 4900) 损失值: 1.051832\n",
      "(迭代 4401 / 4900) 损失值: 1.188053\n",
      "(周期 18 / 20) 训练精度: 0.635000; 验证精度: 0.512000\n",
      "(迭代 4601 / 4900) 损失值: 1.057414\n",
      "(周期 19 / 20) 训练精度: 0.637000; 验证精度: 0.504000\n",
      "(迭代 4801 / 4900) 损失值: 1.085507\n",
      "(周期 20 / 20) 训练精度: 0.629000; 验证精度: 0.509000\n"
     ]
    }
   ],
   "source": [
    "model = None\n",
    "trainer = None\n",
    "\n",
    "D,H,C,std,r= 3*32*32,200,10,1e-2,0.6\n",
    "model = FullyConnectedNet(input_dim=D, hidden_dims=[H], num_classes=C, weight_scale=std)\n",
    "trainer = Trainer(model,data,update_rule='sgd',\n",
    "                updater_config={'learning_rate': 1e-3,},\n",
    "                lr_decay=0.95,num_epochs=20, batch_size=200,print_every=200)\n",
    "trainer.train()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "44fe4385",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 可视化训练/验证结果\n",
    "plt.subplot(2, 1, 1)\n",
    "plt.title('Training loss')\n",
    "plt.plot(trainer.loss_history, 'o')\n",
    "plt.xlabel('Iteration')\n",
    "\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.title('Accuracy')\n",
    "plt.plot(trainer.train_acc_history, '-o', label='train')\n",
    "plt.plot(trainer.val_acc_history, '-o', label='val')\n",
    "plt.plot([0.5] * len(trainer.val_acc_history), 'k--')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(loc='lower right')\n",
    "plt.gcf().set_size_inches(15, 12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bba8cbac",
   "metadata": {},
   "source": [
    "# 正则化实验\n",
    "\n",
    "使用500训练数据进行正则化实验"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d7986fa7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dropout激活概率(0表示不使用dropout)0.000000:\n",
      "(迭代 1 / 150) 损失值: 8.785505\n",
      "(周期 0 / 30) 训练精度: 0.182000; 验证精度: 0.159000\n",
      "(周期 1 / 30) 训练精度: 0.230000; 验证精度: 0.186000\n",
      "(周期 2 / 30) 训练精度: 0.400000; 验证精度: 0.250000\n",
      "(周期 3 / 30) 训练精度: 0.410000; 验证精度: 0.244000\n",
      "(周期 4 / 30) 训练精度: 0.452000; 验证精度: 0.213000\n",
      "(周期 5 / 30) 训练精度: 0.672000; 验证精度: 0.260000\n",
      "(周期 6 / 30) 训练精度: 0.704000; 验证精度: 0.280000\n",
      "(周期 7 / 30) 训练精度: 0.794000; 验证精度: 0.281000\n",
      "(周期 8 / 30) 训练精度: 0.888000; 验证精度: 0.278000\n",
      "(周期 9 / 30) 训练精度: 0.946000; 验证精度: 0.289000\n",
      "(周期 10 / 30) 训练精度: 0.962000; 验证精度: 0.282000\n",
      "(周期 11 / 30) 训练精度: 0.966000; 验证精度: 0.272000\n",
      "(周期 12 / 30) 训练精度: 0.974000; 验证精度: 0.293000\n",
      "(周期 13 / 30) 训练精度: 0.992000; 验证精度: 0.298000\n",
      "(周期 14 / 30) 训练精度: 1.000000; 验证精度: 0.286000\n",
      "(周期 15 / 30) 训练精度: 1.000000; 验证精度: 0.296000\n",
      "(周期 16 / 30) 训练精度: 1.000000; 验证精度: 0.300000\n",
      "(周期 17 / 30) 训练精度: 1.000000; 验证精度: 0.297000\n",
      "(周期 18 / 30) 训练精度: 1.000000; 验证精度: 0.299000\n",
      "(周期 19 / 30) 训练精度: 1.000000; 验证精度: 0.303000\n",
      "(周期 20 / 30) 训练精度: 1.000000; 验证精度: 0.299000\n",
      "(周期 21 / 30) 训练精度: 1.000000; 验证精度: 0.291000\n",
      "(周期 22 / 30) 训练精度: 1.000000; 验证精度: 0.299000\n",
      "(周期 23 / 30) 训练精度: 1.000000; 验证精度: 0.289000\n",
      "(周期 24 / 30) 训练精度: 1.000000; 验证精度: 0.291000\n",
      "(周期 25 / 30) 训练精度: 1.000000; 验证精度: 0.298000\n",
      "(周期 26 / 30) 训练精度: 1.000000; 验证精度: 0.291000\n",
      "(周期 27 / 30) 训练精度: 1.000000; 验证精度: 0.292000\n",
      "(周期 28 / 30) 训练精度: 1.000000; 验证精度: 0.290000\n",
      "(周期 29 / 30) 训练精度: 1.000000; 验证精度: 0.290000\n",
      "(周期 30 / 30) 训练精度: 1.000000; 验证精度: 0.292000\n",
      "dropout激活概率(0表示不使用dropout)0.300000:\n",
      "(迭代 1 / 150) 损失值: 16.429385\n",
      "(周期 0 / 30) 训练精度: 0.176000; 验证精度: 0.161000\n",
      "(周期 1 / 30) 训练精度: 0.274000; 验证精度: 0.177000\n",
      "(周期 2 / 30) 训练精度: 0.358000; 验证精度: 0.222000\n",
      "(周期 3 / 30) 训练精度: 0.428000; 验证精度: 0.251000\n",
      "(周期 4 / 30) 训练精度: 0.522000; 验证精度: 0.289000\n",
      "(周期 5 / 30) 训练精度: 0.580000; 验证精度: 0.264000\n",
      "(周期 6 / 30) 训练精度: 0.580000; 验证精度: 0.269000\n",
      "(周期 7 / 30) 训练精度: 0.672000; 验证精度: 0.299000\n",
      "(周期 8 / 30) 训练精度: 0.696000; 验证精度: 0.282000\n",
      "(周期 9 / 30) 训练精度: 0.718000; 验证精度: 0.301000\n",
      "(周期 10 / 30) 训练精度: 0.754000; 验证精度: 0.288000\n",
      "(周期 11 / 30) 训练精度: 0.762000; 验证精度: 0.292000\n",
      "(周期 12 / 30) 训练精度: 0.782000; 验证精度: 0.290000\n",
      "(周期 13 / 30) 训练精度: 0.798000; 验证精度: 0.308000\n",
      "(周期 14 / 30) 训练精度: 0.798000; 验证精度: 0.314000\n",
      "(周期 15 / 30) 训练精度: 0.814000; 验证精度: 0.292000\n",
      "(周期 16 / 30) 训练精度: 0.858000; 验证精度: 0.316000\n",
      "(周期 17 / 30) 训练精度: 0.884000; 验证精度: 0.303000\n",
      "(周期 18 / 30) 训练精度: 0.878000; 验证精度: 0.307000\n",
      "(周期 19 / 30) 训练精度: 0.900000; 验证精度: 0.314000\n",
      "(周期 20 / 30) 训练精度: 0.930000; 验证精度: 0.306000\n",
      "(周期 21 / 30) 训练精度: 0.938000; 验证精度: 0.310000\n",
      "(周期 22 / 30) 训练精度: 0.934000; 验证精度: 0.309000\n",
      "(周期 23 / 30) 训练精度: 0.934000; 验证精度: 0.329000\n",
      "(周期 24 / 30) 训练精度: 0.950000; 验证精度: 0.307000\n",
      "(周期 25 / 30) 训练精度: 0.906000; 验证精度: 0.296000\n",
      "(周期 26 / 30) 训练精度: 0.936000; 验证精度: 0.286000\n",
      "(周期 27 / 30) 训练精度: 0.970000; 验证精度: 0.329000\n",
      "(周期 28 / 30) 训练精度: 0.946000; 验证精度: 0.309000\n",
      "(周期 29 / 30) 训练精度: 0.966000; 验证精度: 0.301000\n",
      "(周期 30 / 30) 训练精度: 0.978000; 验证精度: 0.303000\n",
      "dropout激活概率(0表示不使用dropout)0.700000:\n",
      "(迭代 1 / 150) 损失值: 10.933135\n",
      "(周期 0 / 30) 训练精度: 0.154000; 验证精度: 0.125000\n",
      "(周期 1 / 30) 训练精度: 0.294000; 验证精度: 0.203000\n",
      "(周期 2 / 30) 训练精度: 0.432000; 验证精度: 0.244000\n",
      "(周期 3 / 30) 训练精度: 0.536000; 验证精度: 0.256000\n",
      "(周期 4 / 30) 训练精度: 0.560000; 验证精度: 0.260000\n",
      "(周期 5 / 30) 训练精度: 0.650000; 验证精度: 0.294000\n",
      "(周期 6 / 30) 训练精度: 0.556000; 验证精度: 0.230000\n",
      "(周期 7 / 30) 训练精度: 0.778000; 验证精度: 0.305000\n",
      "(周期 8 / 30) 训练精度: 0.742000; 验证精度: 0.279000\n",
      "(周期 9 / 30) 训练精度: 0.826000; 验证精度: 0.306000\n",
      "(周期 10 / 30) 训练精度: 0.868000; 验证精度: 0.284000\n",
      "(周期 11 / 30) 训练精度: 0.890000; 验证精度: 0.295000\n",
      "(周期 12 / 30) 训练精度: 0.912000; 验证精度: 0.319000\n",
      "(周期 13 / 30) 训练精度: 0.928000; 验证精度: 0.319000\n",
      "(周期 14 / 30) 训练精度: 0.932000; 验证精度: 0.315000\n",
      "(周期 15 / 30) 训练精度: 0.974000; 验证精度: 0.303000\n",
      "(周期 16 / 30) 训练精度: 0.964000; 验证精度: 0.307000\n",
      "(周期 17 / 30) 训练精度: 0.964000; 验证精度: 0.297000\n",
      "(周期 18 / 30) 训练精度: 0.982000; 验证精度: 0.293000\n",
      "(周期 19 / 30) 训练精度: 0.992000; 验证精度: 0.320000\n",
      "(周期 20 / 30) 训练精度: 0.988000; 验证精度: 0.312000\n",
      "(周期 21 / 30) 训练精度: 0.992000; 验证精度: 0.305000\n",
      "(周期 22 / 30) 训练精度: 0.994000; 验证精度: 0.320000\n",
      "(周期 23 / 30) 训练精度: 0.984000; 验证精度: 0.314000\n",
      "(周期 24 / 30) 训练精度: 0.994000; 验证精度: 0.324000\n",
      "(周期 25 / 30) 训练精度: 0.996000; 验证精度: 0.322000\n",
      "(周期 26 / 30) 训练精度: 0.998000; 验证精度: 0.316000\n",
      "(周期 27 / 30) 训练精度: 0.994000; 验证精度: 0.306000\n",
      "(周期 28 / 30) 训练精度: 0.996000; 验证精度: 0.321000\n",
      "(周期 29 / 30) 训练精度: 0.996000; 验证精度: 0.331000\n",
      "(周期 30 / 30) 训练精度: 1.000000; 验证精度: 0.304000\n"
     ]
    }
   ],
   "source": [
    "num_train = 500\n",
    "small_data = {\n",
    "    'X_train': data['X_train'][:num_train],\n",
    "    'y_train': data['y_train'][:num_train],\n",
    "    'X_val': data['X_val'],\n",
    "    'y_val': data['y_val'],\n",
    "}\n",
    "\n",
    "solvers = {}\n",
    "dropout_choices = [0, 0.3,0.7]\n",
    "for dropout in dropout_choices:\n",
    "    model = FullyConnectedNet(hidden_dims=[600], dropout=dropout)\n",
    "    print(\"dropout激活概率(0表示不使用dropout)%f:\"%dropout)\n",
    "\n",
    "    trainer = Trainer(model, small_data,\n",
    "                                    num_epochs=30, batch_size=100,\n",
    "                                    update_rule='sgd',\n",
    "                                    updater_config={\n",
    "                                        'learning_rate': 5e-4,\n",
    "                                    },\n",
    "                                    verbose=True, print_every=200)\n",
    "    trainer.train()\n",
    "    solvers[dropout] = trainer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "da9b2069",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ogYIoCIUwLX6NzBOmpRaQM0j8ACDLpLrwCgVREAphWvwamSeIiq5AkjHUE0Dard3UyOLq/RTEwuedz8P8O2Q1emSQSkGtrwgkEYkfgLRau6lRV63eotb2DklSY3Orrlq9RZJI/uIIauFz5t/lsLDMiwvb4tfILJ2fiTB8VpAzSPwApNXSdVu7kr5Ore0dWrpuK4lfHEEVXkGO6l2psHNenJR9F7T0yCDVyuZn3+cCOY05fgDSamdza0LtuY7CK0ipoObF1a+UbpgsVRdFb1NRcIXFrwGgB3r8AKTVmKKIGuMkeWOKImmIJvNVTa+Ku/A5hVeQFEHMiwuyV5EeGQDoQo8fgLRaPKdUkfy8Hm2R/DwtnlOapogGLtXVNqXo3LvqE6s1unC0TKbRhaNVfWI18/GQHEFUKqTaJgCkBT1+ANKqcx5ftlf1DKLaZicKryBlgpgXR7VNAEgLEj8AaXfWtLFZl+j1FlS1TSClgqhUSLVNAEgLEj8ASAKqbSLlglpmIdXz4qi2CQBpwRw/AEgCqm0ipToLouzdIck/LYiSimqYqUa1TQBIC3r8ACAJqLaJlDpYQZRsTJiotgkAgSPxA4Ak6JzHV7OxRrtadqm4sFhV06uY34fkoCAKAGCQSPwAIEmotomUoSAKAGCQmOMHAECmm70kWgClOwqiAAASQOIHAECmoyAKAGCQGOoJAEA2oCAKAGAQ6PEDkBNqt9eqYlWFyu4sU8WqCtVur013SAAAAIGhxw9A6NVur+2x1EJTS5Oqn6yWJIqxAACAnECPH4DQq9lY02N9PUlq62hTzcaaNEUEAAAQLBI/AKG3q2VXQu1AwupXSjdMlqqLorf1K9MdEQAAPZD4AQi94sLihNqBhNSvlB64IrbOnkdvH7iC5A8AkFFI/ACEXtX0KhXkFfRoK8grUNX0qjRFhFB59BqpvbVnW3trtB0AgAyRlsTPzE43s61mts3Mrozz+Egze8DMXjCzl8zs4nTECSAcKidUqvrEao0uHC2TaXThaFWfWE1hFyTH3obE2gEASIPAq3qaWZ6kmyWdJqlB0nNmdr+7v9xts8slvezuZ5jZUZK2mtnd7r4v6HgBhEPlhEoSvUxTvzLaK7a3QRpZIs1ekpp16lJ9nJElsWGecdoBAMgQ6VjOYaakbe6+XZLMbIWkuZK6J34u6VAzM0kjJL0r6ZOgAwVy3dpNjVq6bqt2NrdqTFFEi+eU6qxpY9MdFsKgc15c5xDJznlxUnKTsiCOM3tJz2NIUn4k2g4AQIZIx1DPsZK6fzXaEGvrbpmkL0raKWmLpCp33x9MeACkaNJ31eotamxulUtqbG7VVau3aO2mxnSHhjAIal5cEMcpmy+dcZM0cpwki96ecVNqei8BABigdPT4WZw273V/jqTNkr4u6fOSHjGz37v7+wc8mdmlki6VpGOOOSa5kQI5bOm6rWpt7+jR1treoaXrttLrh8ELal5cUMcpm0+iBwDIaOno8WuQNK7b/RJFe/a6u1jSao/aJukNSZPiPZm73+ru5e5eftRRR6UkYCAX7WxuTagdIZPqden6mv+W7HlxQR0HAIAMl47E7zlJXzCz8WY2TNJ5ku7vtc1bkmZLkpkdLalU0vZAowRy3JiiiIYetkmFn79OIyZdqcLPX6ehh23SmKJIukNDqgWxLt3sJdF5cN2lYl5cUMcBACDDBZ74ufsnkn4gaZ2kVyStdPeXzGyRmS2KbfaPkk40sy2SHpX0Y3d/J+hYgVxWMbNRBaNXa8iwZplJQ4Y1q2D0alXMZI5f6IVpXhzz7wAAkCSZe+/pddmrvLzc6+rq0h0GEAoVqyrU1NJ0QPvowtFaf+76NESEwFQX6cCp15JkUnVzsLEAAICEmNnz7l7euz0tC7gDyHy7WnYl1I4QYV4cAAChQ+IHIK7iwuKE2hGgVBdeYV4cAAChQ+IHIK6q6VUqyCvo0VaQV6Cq6VVpigiSgim8wrw4AABCJx3r+AHIApUTKiVJNRtrtKtll4oLi1U1vaqrHXHUr4wWQNnbEB0WOXtJ8pOlgxVeSeaxWJcOAIBQIfED0KfKCZUkev3V2RPXmZR19sRJyU2gglqQHAAAhApDPQEgGYJYAkGi8AoAABgQEj8ASIageuIovAIAAAaAxA9A2tVur1XFqgqV3VmmilUVqt1em+6QEhdUTxyFVwAAwAAwxw9AWtVur1X1k9Vq62iTJDW1NKn6yWpJyq75hbOX9JzjJ6WuJ47CKwAAIEH0+AFIq5qNNV1JX6e2jjbVbKxJU0QDRE8cAADIYPT4AUirXS27EmrPaPTEAQCADEWPH4C0Ki4sTqgdAAAAiSPxA5BWVdOrVJBX0KOtIK9AVdOr0hQRAABA+DDUE0BadRZwqdlYo10tu1RcWKyq6VXJL+xSvzK6pt7ehmilzdlLGJYJAAByBokfgLSrnFCZ2gqe9St7VtzcuyN6XyL5AwAAOYGhngDC79Frei6zIEXvP3pNeuIBAAAIGD1+QJZau6lRS9dt1c7mVo0pimjxnFKdNW1susPKTHsbEmsHAAAIGXr8gCy0dlOjrlq9RY3NrXJJjc2tumr1Fq3d1Jju0DLTyJLE2gEAAEKGxA/IQkvXbVVre0ePttb2Di1dtzVNEQ1S/UrphslSdVH0tn5lcp9/9hIpP9KzLT8SbQcAAMgBDPUEstDO5taE2jNaEIVXOp+Hqp4AACBHkfgBWWhMUUSNcZK8MUWROFtnuIMVXklmYlY2n0QPAADkLIZ6Allo8ZxSRfLzerRF8vO0eE5pmiIaBAqvAAAApBw9fkAW6qzeGYqqniNLosM747UDAAAgKUj8gCx11rSx2Zno9TZ7Sc85fhKFVwAAAJKMoZ4A0qtsvnTGTdLIcZIsenvGTczHAwAASCJ6/IAkC9XC6vUrg6mESeEVAACAlCLxA5Koc2H1zjX2OhdWl5R9yV8QyywAAAAgEAz1BJIoVAurH2yZBQAAAGQVEj8giUK1sDrLLAAAAIQGiR+QRH0toJ6VC6v3tZwCyywAAABkHRI/IIkWzynV8MNfUOHnr9OISVeq8PPXafjhL2Tnwuqzl0SXVeiOZRYAAACy0qASPzP7KzMjeQRi8kduVsHo1RoyrFlm0pBhzSoYvVr5IzenO7TEscwCAABAaJi7D3xns/+U9BVJ90q6w91fSVZgA1FeXu51dXXpDAE5rmJVhZpamg5oH104WuvPXZ+GiAAAAJBLzOx5dy/v3T6o3jp3/1+Spkn6g6Q7zOwpM7vUzA4dzPMC2WpXy66E2gEAAIAgDHqYpru/r2iP3wpJoyWdLWmjmf3tYJ8byDbFhcUJtQMAAABBGOwcvzPMbI2kxyTlS5rp7t+Q9CVJf5eE+ICsUjW9SgV5BT3aCvIKVDW9Kk0RAQAAANLQQe7/LUk3uPv/dG9094/M7LuDfG4g61ROqJQk1Wys0a6WXSouLFbV9KqudgAAACAdBlvcZbykJndvi92PSDra3d9MTniJobgLAAAAgFyWkuIukn4taX+3+x2xNgAAAABAhhhs4jfU3fd13on9PGyQzwkAAAAASKLBJn5vm9mZnXfMbK6kdwb5nAAAAACAJBpscZdFku42s2WSTNIOSRcNOioAAAAAQNIMdgH3P7j7lyX9uaQ/d/cT3X3bZ+1nZqeb2VYz22ZmV/axzdfMbLOZvWRmvxtMnAAAAACQywbb4yczq5R0vKQCM5Mkufs1B9k+T9LNkk6T1CDpOTO7391f7rZNkaRfSDrd3d8ys88NNk4AAAAAyFWDXcD9FknflvS3ig71/JakYz9jt5mStrn79lgxmBWS5vba5gJJq939LUly992DiRMAAAAActlgi7uc6O4XSXrP3a+W9BVJ4z5jn7GKzgXs1BBr6+7PJB1uZo+b2fNmxrxBAAAAABigwQ71bIvdfmRmYyTtkTT+M/axOG29V5EfKukvJM2WFJH0lJk97e6vHfBkZpdKulSSjjnmmARCBwAAAIDcMNgevwdi8/GWStoo6U1J93zGPg3q2StYImlnnG0edvcWd39H0v9I+lK8J3P3W9293N3LjzrqqMRfAQAAAACE3IATPzMbIulRd29293sVnds3yd2XfMauz0n6gpmNN7Nhks6TdH+vbe6T9FUzG2pmwyWdIOmVgcYKBKp+pXTDZKm6KHpbvzK7jwMAAICsN+Chnu6+38x+pui8Prn7x5I+7sd+n5jZDyStk5Qn6XZ3f8nMFsUev8XdXzGzhyXVS9ov6Zfu/uJAYwUCU79SeuAKqb01en/vjuh9SSqbn33HAQAAQCiYe+/pdQnsbHa1osnZah/MEyVJeXm519XVpTsM5LIbJkeTsN5GjpN+lMTvLoI6DgAAALKKmT3v7uW92wdb3OV/SyqU9ImZtSlauMXd/bBBPi+QnfY2JNae6ccBAABAKAyquIu7H+ruQ9x9mLsfFrtP0ofcNbIksfZMPw4AAABCYbALuJ8c71+yggOSrXZ7rSpWVajszjJVrKpQ7fba5B5g9hIpP9KzLT8Sbc/G4wAAACAUBjvUc3G3nwskzZT0vKSvD/J5gaSr3V6r6ier1dYRXX6yqaVJ1U9WS5IqJ1Qm5yCdhVUevSY67HJkSTQZS3bBlaCOAwAAgFAYVOLn7md0v29m4yT9y6AiAlKkZmNNV9LXqa2jTTUba5KX+EnR5CuABKx2RKFqxo3RriOGqLiwWFUjCpXEVwEAAIAQGWyPX28NkiYn+TmBpNjVsiuh9kwWSO8lAAAAQmNQiZ+Z/V9Jncs4DJE0VdILg4wJSIniwmI1tTTFbc82gfVeAgAAIBQGVdxFUp2ic/qel/SUpB+7+/8adFRAClRNr1JBXkGPtoK8AlVNr0pTRAMXpt5LAAAApN5gh3quktTm7h2SZGZ5Zjbc3T8afGhAcnX2hNVsrNGull3ReXHTq7KyhyxMvZcAAABIvcEmfo9K+ktJH8buRyStl3TiIJ8XOah2e23Kk7LKCZVZmej1VjW9qsccPyl7ey8BAACQeoNN/ArcvTPpk7t/aGbDB/mcyEEUK0lMmHovAQAAkHqDTfxazGy6u2+UJDP7C0mtgw8LuYZiJYkLS+8lAAAAUm+wid8PJf3azHbG7o+W9O1BPidyUGDFSupXsug5AAAAcs5gF3B/zswmSSqVZJJedff2pESGnBJIsZL6ldIDV0jtsU7pvTui9yWSPwAAAITaoJZzMLPLJRW6+4vuvkXSCDP7fnJCQy4JZKmFR6/5NOnr1N4abQcAAABCbLDr+H3P3Zs777j7e5K+N8jnRA6qnFCp6hOrNbpwtEym0YWjVX1idXLnsO1tSKwdAAAACInBzvEbYmbm7i5F1/GTNGzwYSEXpbxYyciS6PDOeO0AAABAiA22x2+dpJVmNtvMvi7pHkkPDT4sIAVmL5HyIz3b8iPRdgAAACDEBtvj92NJl0q6TNHiLpsUrewJZJ7OAi5U9QQAAECOGWxVz/1m9rSkCYou43CEpHuTERiQEmXzSfQAAACQcwaU+JnZn0k6T9L5kvZI+pUkufupyQsNAAAAAJAMA+3xe1XS7yWd4e7bJMnMfpS0qAAAAAAASTPQ4i7nSNol6bdmdpuZzVZ0jh8AAAAAIMMMKPFz9zXu/m1JkyQ9LulHko42s381s4okxgcAAAAAGKRBLefg7i3ufre7/5WkEkmbJV2ZjMAAAAAAAMkx2HX8urj7u+7+b+7+9WQ9JwAAAABg8JKW+AEAAAAAMhOJHwAAAACEHIkfAAAAAIQciR8AAAAAhNxAF3AHstLaTY1aum6rdja3akxRRIvnlOqsaWPTHRYAAACQUiR+yBlrNzXqqtVb1NreIUlqbG7VVau3SBLJHwAAAEKNoZ7IGUvXbe1K+jq1tndo6bqtaYoIAAAACAaJH3LGzubWhNoBAACAsCDxQ84YUxRJqB0AAAAICxI/fKba7bWqWFWhsjvLVLGqQrXba9Md0oAsnlOqSH5ej7ZIfp4WzylNU0QAAABAMCjugoOq3V6r6ier1dbRJklqamlS9ZPVkqTKCZXJPVj9SunRa6S9DdLIEmn2EqlsftKevrOAC1U9AQAAkGvM3dMdQ9KUl5d7XV1dusMIlYpVFWpqaTqgfXThaK0/d33yDlS/UnrgCqm923y7/Ih0xk1JTf4AAACAMDOz5929vHc7Qz1xULtadiXUPmCPXtMz6ZOi9x+9JrnHAQAAAHIQiR8OqriwOKH2AdvbkFg7AAAAgH4j8cNBVU2vUkFeQY+2grwCVU2vSu6BRpYk1g4AAACg30j8cFCVEypVfWK1RheOlsk0unC0qk+sTn5hl9lL9EmvBPOTvIJogRcAAAAAg0JVT3ymygmVyU/0elnbMUsb2i/RD7VCY2yPdvoo3bj/PJ3UMUtnpfTIAAAAQPilJfEzs9Ml1UjKk/RLd7+uj+1mSHpa0rfdfVWAISJgS9dtVeO+E7VKJ/Zof2rdVpZbAAAAAAYp8MTPzPIk3SzpNEkNkp4zs/vd/eU42/2zpHVBx4ie1m5qTPnadzubWxNqBwAAANB/6ZjjN1PSNnff7u77JK2QNDfOdn8r6V5Ju4MMDj2t3dSoq1ZvUWNzq1xSY3Orrlq9RWs3NSb1OGOKIgm1AwAAAOi/dCR+YyXt6Ha/IdbWxczGSjpb0i0BxoU4lq7bqtb2jh5tre0dWrpua1KPs3hOqSL5eT3aIvl5WjynNKnHAQAAAHJROub4WZw273X/Rkk/dvcOs3ibd3sys0slXSpJxxxzTDLiQzdBDcHsHDqa6iGlAAAAQC5KR+LXIGlct/slknb22qZc0opY0nekpG+a2Sfuvrb3k7n7rZJulaTy8vLeCSQGaUxRRI1xkrxUDME8a9pYEj0AAAAgBdIx1PM5SV8ws/FmNkzSeZLu776Bu4939+Pc/ThJqyR9P17Sh9RjCCYAAACQ/QLv8XP3T8zsB4pW68yTdLu7v2Rmi2KPM68vgzAEEwAAAMh+5h6e0ZHl5eVeV1eX7jAAAAAAIC3M7Hl3L+/dno6hngAAAACAAJH4AQAAAEDIkfgBAAAAQMiR+OGz1a+UbpgsVRdFb+tXpjsiAAAAAAlIxzp+yCb1K6UHrpDaY2v57d0RvS9JZfPTFxcAAACAfqPHDwf36DWfJn2d2luj7QAAAACyAokfDm5vQ2LtAAAAADIOiR8ObmRJYu0AAAAAMg6JHw5u9hIpP9KzLT8SbQcAAACQFUj8cHBl86UzbpJGjpNk0dszbqKwCwAAAJBFqOqJz1Y2n0QPAAAAyGL0+AEAAABAyJH4AQAAAEDIkfgBAAAAQMiR+AEAAABAyJH4AQAAAEDIkfgBAAAAQMiR+AEAAABAyJH4AQAAAEDIkfhludrttapYVaGyO8tUsapCtdtr0x0SAAAAgAwzNN0BYOBqt9eq+slqtXW0SZKaWppU/WS1JKlyQmUaIwMAAACQSejxy2I1G2u6kr5ObR1tqtlYk6aIAAAAAGQiEr8stqtlV0LtAAAAAHITiV8WKy4sTqgdAAAAQG4i8ctiVdOrVGD5PdoKLF9V06vSFBEAAACATERxlyxW+WGL9M4e1Rw2XLuG5qn4kw5Vvf9+tB0AAAAAYkj8stmj16jy/WZVvt98QLvK5qclJAAAAACZh6Ge2WxvQ2LtAAAAAHISiV82G1mSWDsAAACAnETil81mL5HyIz3b8iPRdgAAAACIIfHLZmXzpTNukkaOk2TR2zNuYn4fAAAAgB4o7pLtyuaT6AEAgKzQ3t6uhoYGtbW1pTsUIOsVFBSopKRE+fn5n72xSPwAAAAQkIaGBh166KE67rjjZGbpDgfIWu6uPXv2qKGhQePHj+/XPgz1BAAAQCDa2to0atQokj5gkMxMo0aNSqj3nMQPAAAAgSHpA5Ij0c8SiR8AAAByxsMPP6zS0lJNnDhR1113Xdxt3F1XXHGFJk6cqLKyMm3cuDGh/XsbMWJEUmIfiOXLl2vnzp1pO36y9ef9X7p0qaZOnaqpU6dq8uTJysvL07vvvtvv/XsLy/kj8ctyazc1atZ1j2n8lbWadd1jWrupMd0hAQAAZKSOjg5dfvnleuihh/Tyyy/rnnvu0csvv3zAdg899JBef/11vf7667r11lt12WWXJbR/f2MJQpgSv/6+/4sXL9bmzZu1efNmXXvttTrllFN0xBFH5Pz5I/HLYms3Neqq1VvU2Nwql9TY3KqrVm8h+QMAAKGQ7C+4n332WU2cOFETJkzQsGHDdN555+m+++47YLv77rtPF110kcxMX/7yl9Xc3KympqZ+7//GG2/oK1/5imbMmKGf/OQnXe2PP/64Tj31VF1wwQWaMmWK2tradPHFF2vKlCmaNm2afvvb30qKXuzPnTtXp59+ukpLS3X11Vd3PcfPf/5zTZ48WZMnT9aNN94oSXrzzTc1efLkrm2uv/56VVdXa9WqVaqrq9OFF16oqVOnqrW1dVDvX6Jqt9eqYlWFyu4sU8WqCtVurx3U8/X3/e/unnvu0fnnn5/Q/mE9f1T1zGJL121Va3vPbxta2zu0dN1WnTVtbJqiAgAAGLzOL7g7r3U6v+CWNODrnMbGRo0bN67rfklJiZ555pl+bdfY2Njv/auqqnTZZZfpoosu0s0339zjsWeffVYvvviixo8fr5/97GeSpC1btujVV19VRUWFXnvttR7bDR8+XDNmzFBlZaXMTHfccYeeeeYZubtOOOEEnXLKKTr88MPjvt5zzz1Xy5Yt0/XXX6/y8vIE3qnBq91eq+onq9XWES0+0tTSpOonqyVJlRMqB/Sc/X3/O3300Ud6+OGHtWzZsoT2D+v5o8cvi+1sjp/199UOAACQLQ72BfdAufsBbfEKZPS1XX/3f+KJJ7p6mRYsWNDjsZkzZ3aV39+wYUPX45MmTdKxxx7blTicdtppGjVqlCKRiObNm6cNGzZow4YNOvvss1VYWKgRI0Zo3rx5+v3vf/9ZLzstajbWdCV9ndo62lSzsWbAz9nf97/TAw88oFmzZumII45IaP+wnj96/LLYmKKIGuMkeWOKImmIBgAAIHlS8QV3SUmJduzY0XW/oaFBY8aM6fd2+/bt69f+Ut8JSWFhYdfP8RKRvvbvK/GUpKFDh2r//v1d9xMp8Z8qu1p2JdTeH/09f51WrFjRlcAlun8Yzx89flls8ZxSRfLzerRF8vO0eE5pmiICAABIjr6+yB7MF9wzZszQ66+/rjfeeEP79u3TihUrdOaZZx6w3Zlnnqm77rpL7q6nn35aI0eO1OjRo/u9/6xZs7RixQpJ0t13391nPCeffHLX46+99preeustlZZGr+MeeeQRvfvuu2ptbdXatWs1a9YsnXzyyVq7dq0++ugjtbS0aM2aNfrqV7+qo48+Wrt379aePXv08ccf68EHH+w6xqGHHqoPPvhgwO/ZQBUXFifU3h/9ff8lae/evfrd736nuXPnJrx/WM9fWhI/MzvdzLaa2TYzuzLO4xeaWX3s35Nm9qV0xJnpzpo2VtfOm6KxRRGZpLFFEV07bwrz+wAAQNZLxRfcQ4cO1bJlyzRnzhx98Ytf1Pz583X88cdLkm655RbdcsstkqRvfvObmjBhgiZOnKjvfe97+sUvfvGZ+3dXU1Ojm2++WTNmzNDevXv7jOf73/++Ojo6NGXKFH3729/W8uXLdcghh0iSTjrpJC1YsEBTp07VOeeco/Lyck2fPl0LFy7UzJkzdcIJJ+iSSy7RtGnTlJ+fryVLluiEE07QX/3VX2nSpEldx1i4cKEWLVoUeHGXqulVKsgr6NFWkFegqulVA37O/p4/SVqzZo0qKip69NDl+vmzg3VRpoKZ5Ul6TdJpkhokPSfpfHd/uds2J0p6xd3fM7NvSKp29xM+67nLy8u9rq4uRZEDAABgMF555RV98Ytf7Pf2azc1aum6rdrZ3KoxRREtnlOaE19wL1++XHV1dV1FSbJV7fZa1Wys0a6WXSouLFbV9KoBF3bJJkGev3ifKTN73t0PqAaTjjl+MyVtc/ftkmRmKyTNldSV+Ln7k922f1pSSaARAgAAIO3OmjY2JxK9sKqcUJkTiV62SEfiN1bSjm73GyQdrDfvryU9lNKIAAAAgAyxcOFCLVy4MN1hYIAy9fylI/GLVyIn7nhTMztV0cTvpD6fzOxSSZdK0jHHHJOM+AAAAAAgVNJR3KVB0rhu90sk7ey9kZmVSfqlpLnuvqevJ3P3W9293N3LjzrqqKQHCwAAAADZLh09fs9J+oKZjZfUKOk8SRd038DMjpG0WtICd38t+BCTI1cnJAMAAADILIEnfu7+iZn9QNI6SXmSbnf3l8xsUezxWyQtkTRK0i9iix9+Eq8yTSZbu6lRG9b8Qr/SCo055B3t/OhI3bjmPEnfJ/kDAAAAEKi0rOPn7r9x9z9z98+7+z/F2m6JJX1y90vc/XB3nxr7l1VJnyRtrr1V19itKhnyjoaYVDLkHV1jt2pz7a3pDg0AACBnPfzwwyotLdXEiRN13XXXxd3mvvvuU1lZmaZOnary8nJt2LAhof17GzFiRFJiH4jly5dr584DZlUhB6Ul8csFl+z7Tw23fT3ahts+XbLvP9MUEQAAQG7r6OjQ5Zdfroceekgvv/yy7rnnHr388ssHbDd79my98MIL2rx5s26//XZdcsklCe3f31iCQOKHTiR+KTJmSPx6NH21AwAAoJf6ldINk6Xqouht/cpBPd2zzz6riRMnasKECRo2bJjOO+883XfffQdsN2LECMWmG6mlpaXr5/7u/8Ybb+grX/mKZsyYoZ/85Cdd7Y8//rhOPfVUXXDBBZoyZYra2tp08cUXa8qUKZo2bZp++9vfSooma3PnztXpp5+u0tJSXX311V3P8fOf/1yTJ0/W5MmTdeONN0qS3nzzTU2ePLlrm+uvv17V1dVatWqV6urqdOGFF2rq1KlqbW0d1PuH7JaO4i45oS1SrOGtTfHb0xAPAABAVqlfKT1whdQeS1b27ojel6Sy+QN6ysbGRo0b92lx+ZKSEj3zzDNxt12zZo2uuuoq7d69W7W1tQntX1VVpcsuu0wXXXSRbr755h6PPfvss3rxxRc1fvx4/exnP5MkbdmyRa+++qoqKir02muv9dhu+PDhmjFjhiorK2VmuuOOO/TMM8/I3XXCCSfolFNO0eGHHx73NZx77rlatmyZrr/+epWXZ93MKSQZPX4pMvwb1+iTvIIebZ/kFWj4N65JU0QAAABZ5NFrPk36OrW3RtsHyP3ApaM7e/N6O/vss/Xqq69q7dq1Xb12/d3/iSee0Pnnny9JWrBgQY/HZs6cqfHjx0uSNmzY0PX4pEmTdOyxx3YlfqeddppGjRqlSCSiefPmacOGDdqwYYPOPvtsFRYWasSIEZo3b55+//vf9/flI8fR45cqZfO17t0tqtm+RruGSMX7paoJZ6tygN9QAQAA5JS9DYm190NJSYl27NjRdb+hoUFjxow56D4nn3yy/vCHP+idd95JaP++EsrCwsKun+Mlkn3tb2Z9bj906FDt37+/635bW1ufz4vcRY9fitRur1V1w8NqyjO5mZryTNUND6t2e226QwMAAMh8I0sSa++HGTNm6PXXX9cbb7yhffv2acWKFTrzzDMP2G7btm1dSdbGjRu1b98+jRo1qt/7z5o1SytWrJAk3X333X3Gc/LJJ3c9/tprr+mtt95SaWmpJOmRRx7Ru+++q9bWVq1du1azZs3SySefrLVr1+qjjz5SS0uL1qxZo69+9as6+uijtXv3bu3Zs0cff/yxHnzwwa5jHHroofrggw8G/J4hPOjxS5GajTVq6+j5bUtbR5tqNtaockJlmqICAADIErOX9JzjJ0n5kWj7AA0dOlTLli3TnDlz1NHRoe9+97s6/vjjJUm33HKLJGnRokW69957dddddyk/P1+RSES/+tWvZGYH3b+7mpoaXXDBBaqpqdE555zTZzzf//73tWjRIk2ZMkVDhw7V8uXLdcghh0iSTjrpJC1YsEDbtm3TBRdc0DVHb+HChZo5c6Yk6ZJLLtG0adMkSUuWLNEJJ5yg8ePHa9KkSV3HWLhwoRYtWqRIJKKnnnpKkUhkwO8fspsdrIs525SXl3tdXV26w5Akld1ZJlecceAy1X+nPg0RAQAApNcrr7yiL37xi/3foX5ldE7f3oZoT9/sJQMu7JJNli9frrq6Oi1btizdoSDDxftMmdnz8dZBp8cvRYoLi9XUcmBVz+LC4jREAwAAkIXK5udEogcEgTl+KVI1vUoFvap6FuQVqGp6VZoiAgAAQDZYuHAhvX1IOnr8UqRzHl/Nxhrtatml4sJiVU2vYn4fAAAAgMCR+KVQ5YRKEj0AAIBu3L3PpQ4A9F+itVoY6gkAAIBAFBQUaM+ePQlfsALoyd21Z88eFRQUfPbGMfT4AQAAIBAlJSVqaGjQ22+/ne5QgKxXUFCgkpL+r2tJ4gcAAIBA5Ofna/z48ekOA8hJDPUEAAAAgJAj8QMAAACAkCPxAwAAAICQszBVVTKztyX9Md1xxHGkpHfSHQQCx3nPXZz73MW5z12c+9zFuc9dmXruj3X3o3o3hirxy1RmVufu5emOA8HivOcuzn3u4tznLs597uLc565sO/cM9QQAAACAkCPxAwAAAICQI/ELxq3pDgBpwXnPXZz73MW5z12c+9zFuc9dWXXumeMHAAAAACFHjx8AAAAAhByJXwqZ2elmttXMtpnZlemOB8ExszfNbIuZbTazunTHg9Qxs9vNbLeZvdit7Qgze8TMXo/dHp7OGJEafZz7ajNrjH32N5vZN9MZI5LPzMaZ2W/N7BUze8nMqmLtfO5D7iDnns99yJlZgZk9a2YvxM791bH2rPrcM9QzRcwsT9Jrkk6T1CDpOUnnu/vLaQ0MgTCzNyWVu3smru2CJDKzkyV9KOkud58ca/sXSe+6+3WxL30Od/cfpzNOJF8f575a0ofufn06Y0PqmNloSaPdfaOZHSrpeUlnSVooPvehdpBzP1987kPNzExSobt/aGb5kjZIqpI0T1n0uafHL3VmStrm7tvdfZ+kFZLmpjkmAEnm7v8j6d1ezXMl3Rn7+U5FLwwQMn2ce4Scuze5+8bYzx9IekXSWPG5D72DnHuEnEd9GLubH/vnyrLPPYlf6oyVtKPb/QbxxyGXuKT1Zva8mV2a7mAQuKPdvUmKXihI+lya40GwfmBm9bGhoBk97AeDY2bHSZom6Rnxuc8pvc69xOc+9Mwsz8w2S9ot6RF3z7rPPYlf6licNsbV5o5Z7j5d0jckXR4bEgYg/P5V0uclTZXUJOlnaY0GKWNmIyTdK+mH7v5+uuNBcOKcez73OcDdO9x9qqQSSTPNbHKaQ0oYiV/qNEga1+1+iaSdaYoFAXP3nbHb3ZLWKDr0F7njT7G5IJ1zQnanOR4ExN3/FLs42C/pNvHZD6XYHJ97Jd3t7qtjzXzuc0C8c8/nPre4e7OkxyWdriz73JP4pc5zkr5gZuPNbJik8yTdn+aYEAAzK4xN+paZFUqqkPTiwfdCyNwv6Tuxn78j6b40xoIAdV4AxJwtPvuhEyvy8O+SXnH3n3d7iM99yPV17vnch5+ZHWVmRbGfI5L+UtKryrLPPVU9UyhWzvdGSXmSbnf3f0pvRAiCmU1QtJdPkoZK+i/OfXiZ2T2SvibpSEl/kvRTSWslrZR0jKS3JH3L3SkCEjJ9nPuvKTrcyyW9KelvOud/IBzM7CRJv5e0RdL+WPM/KDrXi899iB3k3J8vPvehZmZlihZvyVO042ylu19jZqOURZ97Ej8AAAAACDmGegIAAABAyJH4AQAAAEDIkfgBAAAAQMiR+AEAAABAyJH4AQAAAEDIkfgBANCLmXWY2eZu/65M4nMfZ2as8wUACNTQdAcAAEAGanX3qekOAgCAZKHHDwCAfjKzN83sn83s2di/ibH2Y83sUTOrj90eE2s/2szWmNkLsX8nxp4qz8xuM7OXzGy9mUXS9qIAADmBxA8AgANFeg31/Ha3x95395mSlkm6Mda2TNJd7l4m6W5JN8Xab5L0O3f/kqTpkl6KtX9B0s3ufrykZknnpPTVAABynrl7umMAACCjmNmH7j4iTvubkr7u7tvNLF/SLncfZWbvSBrt7u2x9iZ3P9LM3pZU4u4fd3uO4yQ94u5fiN3/saR8d/9/A3hpAIAcRY8fAACJ8T5+7mubeD7u9nOHmHMPAEgxEj8AABLz7W63T8V+flLSebGfL5S0Ifbzo5IukyQzyzOzw4IKEgCA7viGEQCAA0XMbHO3+w+7e+eSDoeY2TOKfnl6fqztCkm3m9liSW9LujjWXiXpVjP7a0V79i6T1JTq4AEA6I05fgAA9FNsjl+5u7+T7lgAAEgEQz0BAAAAIOTo8QMAAACAkKPHDwCQc8zMOxdfBwAgF5D4AQCyjpmtM7Nr4rTPNbNdZkbxMgAAuiHxAwBko+WSFpiZ9WpfIOlud/8k+JCSw8zy0h0DACB8SPwAANloraQjJH21s8HMDpf0V5LuMrOZZvaUmTWbWZOZLTOzYf15YjO72MxeMbMPzGy7mf1Nr8fnmtlmM3vfzP5gZqfH2o8wszvMbKeZvWdma2PtC81sQ6/n6BpqambLzexfzew3ZtYi6VQzqzSzTbFj7DCz6l77n2RmT8Ze347YMWaY2Z+693aa2Tm9lqUAAOQoEj8AQNZx91ZJKyVd1K15vqRX3f0FRdfM+5GkIyV9RdJsSd/v59PvVjSBPEzR9fhuMLPpkmRmMyXdJWmxpCJJJ0t6M7bff0gaLul4SZ+TdEMCL+kCSf8k6VBFF39vib22IkmVki4zs7NiMRwj6SFJ/1fSUZKmStrs7s9J2iPptG7P+79icQEAchxVPQEAWcnMTpJUK6nY3VvN7AlJq9z9gITLzH4o6RR3Pzt23yV9wd239eM4ayX91t1rzOzfJH3k7j/qtc1oSY2SRrn7e70eWyjpEnc/qVtb1/HNbLmkIe7ePYntHcONktzdf2RmV0ma2flaem33Y0ll7n6hmR0hqUHS592dReMBIMfR4wcAyEruvkHS25LmmtkESTMk/ZckmdmfmdmDsUIv70v6/xTt/ftMZvYNM3vazN41s2ZJ3+y27zhJf4iz2zhJ7/ZO+hKwo1cMJ5jZb83sbTPbK2lRP2KQpP+UdIaZjVC0B/T3JH0AAInEDwCQ3e5SdEjkAknr3f1PsfZ/lfSqor1qh0n6B0m9C8EcwMwOkXSvpOslHe3uRZJ+023fHZI+H2fXHZKOMLOiOI+1KDoEtPMYxXG26T385r8k3S9pnLuPlHRLP2KQuzdKekrS2Yq+JwzzBABIIvEDAGS3uyT9paTvSbqzW/uhkt6X9KGZTZJ0WT+fb5ikQxTtSfzEzL4hqaLb4/8u6WIzm21mQ8xsrJlNivWqPSTpF2Z2uJnlm9nJsX1ekHS8mU01swJJ1f2I41BFexDbYvMKL+j22N2S/tLM5pvZUDMbZWZTuz1+l6S/lzRF0pp+vm4AQMiR+AEAspa7vynpSUmFivaQdfo7RZOlDyTdJulX/Xy+DyRdoWjhmPdiz3F/t8efVazgi6S9kn4n6djYwwsktSva07hb0g9j+7wm6RpJ/y3pdUWLt3yW70u6xsw+kLQkFk9nDG8pOvz0/0h6V9JmSV/qtu+aWExr3L2lP68bABB+FHcBACBkzOwPkv7G3f873bEAADIDPX4AAISImZ2j6JzBx9IdCwAgcwz97E0AAEA2MLPHJf25pAXuvj/N4QAAMghDPQEAAAAg5BjqCQAAAAAhR+IHAAAAACEXqjl+Rx55pB933HHpDgMAAAAA0uL5559/x92P6t0eqsTvuOOOU11dXbrDAAAAAIC0MLM/xmtnqCcAAAAAhByJHwAAAACEHIkfAAAAAIQciR8AAAAAhByJHwAAAACEHIkfAAAAAIQciR8AAACArFe7vVYVqypUdmeZKlZVqHZ7bbpDyiihWscPAAAAQO6p3V6r6ier1dbRJklqamlS9ZPVkqTKCZVpjCxz0OMHAACAYNSvlG6YLFUXRW/rV6Y7IoTknNRsrOlK+jq1dbSpZmNNmiLKPPT4AQAAIPXqV0oPXCG1t0bv790RvS9JZfPTF1cuC9E52dWyK6H2XESPHwAAAFLv0Ws+TTA6tbdG25EeITonxYXFCbXnIhI/AAAApN7ehsTakXohOidV06tUkFfQo60gr0BV06vSFFHmYagnAAAAUm9kSXQoYbx2pEeIzklnAZeajTXa1bJLxYXFqppeRWGXbkj8AAAAkHqzl/ScTyZJ+ZFoexLVbq/l4r+/AjonQamcUMm5PggSPwAAAKReZ7GQR6+JDiUcWRJNMJJYRISS/gkK4Jwgc5i7pzuGpCkvL/e6urp0hwEAAIA0qFhVoaaWpgPaRxeO1vpz1yf1WPQsIlOZ2fPuXt67nR4/AAAAhEJQJf3pWUwciXL6UdUTAAAAoRBUSX8WC09MZ6Lc1NIkl3clyrXba9MdWk4h8QMAAKFUu71WFasqVHZnmSpWVXCR+RnC8H4FVdKfxcITQ6KcGRjqCQA4QBBDchj2g1RiKF5iwvJ+BVXSv7iwOO5cQhYLj49EOTPQ4wcA6CGIITkM+0Gq0cOQmDC9X5UTKrX+3PWq/0691p+7PiWJK4uFJyaoIbg4OBI/AEAPQVwAhukiE5mJHobE8H4lpnJCpapPrNbowtEymUYXjlb1idVZ1TsaJBLlzMBQTwBAD0FcAHKROQD1K1lrKwEMxUsM71figlgsPCxD4oMagouDo8cPANBDEENyghz2E4aCFapfKT1whbR3hySP3j5wRbQdcdHDkBjer8wTtiHxQQzBxcGR+AEAegjiAjCoi8za7bWq3vCTnhdOG36SfRdOj14jtbf2bGtvjbYjrsoJlaouOV2jO1zmrtEdruqS07nY7ENQQxdD8UVMQBgSj2RjqCcAoIcghuQENeyn5ulr1ebtPdravF01T1+bXQnA3obE2iHVr1TlE7epsnvC3HSbdMQUhsj2IdVDF8NSOTQoDIlHspH4AQAOEMTclSCOsWtfs2QWvz2bjCyJDfOM0474DtZLmsTELyxzsIJwsB4s3rMDMe8SycZQTwBAaBV/0pFQe8aavUTKj/Rsy49E2xFfAL2kYZuDlWr0YCWGeZdINhI/AEBoVX2cp4L9+3u0Fezfr6qP89IU0QCVzZfOuEkaOU6SRW/PuIkhiwfTV29oEntJA5uDVb9SumGyVF0Uvc3Soj6s5ZYYloxAsjHUEwAQWpVfXSL992LVHDZcu4bmqfiTDlW9/5Eq/3JpukNLXNl8Er1EzF4SrXzafbhnkntJA+nB6qzo2vk6Oiu6Ssn/fUjxkiFV06t6zPGT6MH6LEEMiQ8MS9KkHYkfACA9grgIKJuvSkmV3Y/zl0u52MgFnec4hb9jgczBCmiuYhAJJmu55bAgv8BAn8zd0x1D0pSXl3tdXV26wwAAfJbeFwFStDeG4Yt9ClURkZB889+7SqUU7cFK6nC86iJJ8a7VTKpuTs4xpOgQ0rgFhMZJP3oxeccJQkh+v0IlTL9fWcDMnnf38t7tzPEDkBNYOyrDsC5dQkJVRCREi9EHslZgAHMVJYVnyZAQ/X4FJZD/H8Py+5XlSPwA9CksyVKoLprDgouAhIRqIecwJf2xtQLXv7VD9W/u0Pq3dqjyiduSm2TMXqLaw4pUUTJGZceNU0XJGNUeVpT8iq5BJZipFqbfrwAE9v9jWH6/shyJH4C4wpQsheqiOSy4CEhIqMrghynpDyDJqB1RqOojR6kpf6jcTE35Q1V95CjVjihM2jEkhWfJkDD9fgUgsP8fw/L7FZOtX4yT+AGIK0zJUqgumsMiZBcBqRaqMvhhSvoDSDJqNtaozdt7tLV5e/L/FodlyZAw/X4FILD/H8Py+6Xs/mKcxA9AXGFKlkJ10RwWIboIkFL/7W+gCzmnes24AJP+tZsaNeu6xzT+ylrNuu4xrd3UmNwDBJBkBPq3uGx+tNBGdXP0Nhs/j3yplJBA/38Mw++XsvuLcRI/AHGFKVkK9KIZ/ReSi4Agvv0NbCHnIApjBJT0r93UqKtWb1Fjc6tcUmNzq65avSW5yV8ASUaY/hYHImRfKqUa/z8mblecJVwO1p5JUrqcg5mdLqlGUp6kX7r7db0enyvpHyXtl/SJpB+6+4b+7BsPyzkAyRNImfIA1T7+E9VsX6NdQ6Ti/VLVhLNV+bV/THdYCIGKVRVx13IbXTha689dn4aIBiFEJddnXfeYGptbD2gfWxTRE1d+PXkHSvHSAWH7W4zMw1Ixiam4fbKa8uyA9tEdrvXfzYy/k30t55CyBdzNLE/SzZJOk9Qg6Tkzu9/dX+622aOS7nd3N7MySSslTernvgBSKFQL7cYq71V2L8LQdJt0xBS+BcagBTUUb+2mRi1dt1U7m1s1piiixXNKdda0sUk9RpgKY+yMk/QdrH3Ayuan9O9IqP4WIyNVTqgMx+9TQIvEV+15V9VHHq62IZ8OnCzYv19Ve95L2jFSJWWJn6SZkra5+3ZJMrMVkuZK6kre3P3DbtsX6tMVSj9zXwCpV/lhiyp37Ix9c7Zf+rOWdIc0MAervEfih0EqLiyO2+OXzKF4ncMWW9s7JH06bFFScpO/kSV99PgltzBGEEnsmKJI3B6/MUWROFsPXBCvJagL80C+XABSJaD/6yuHHiG9s0c1hxdp19A8FX/Soar3mlU5dFTSjpEqqZzjN1ZS9/89GmJtPZjZ2Wb2qqRaSd9NZF8AKRSmRXBD1IsRJikvvBGQIObILF23tSvp69Ta3qGl67Ym7RiSApmzFsjcO0mL55Qqkp/Xoy2Sn6fFc0qTdoygXksQwvRaghKWv2GhEdT/9bOXqHKfa33Dzuj6nQ07VbnPs6KAUCoTvwMHv37ao/dpg/sad58k6SxF5/v1e19JMrNLzazOzOrefvvtgcYKoLcwLYJLee+ME6aLzCAKrwQ5bPGmP7tAs0tKVHbcOM0uKdFNf3ZBUr8tDyqJPWvaWF07b4rGFkVkis7tu3belKT2YAWWkAcgTK8lCGH6GxYaQf1fn8UFhFI51LNB0rhu90sk7exrY3f/HzP7vJkdmci+7n6rpFulaHGXwQYNICZMvWSzl/Qc9y9R3jvNDnaRmY1Dy9r3TlXLtiv1QXOrDi2KqP3zyetVkoIbtnj1Y/+hX3/4uCw/+r3w7nzTrR8+rvce+w/99OsLknKMwJJYRZO/VP4+BflaUi1MryUIS9dt1Wkdv9PfD1upMfaOdvqR+pdP5mvpumFZ+TcsFIL8vz7Fc3tTJZU9fs9J+oKZjTezYZLOk3R/9w3MbKKZWezn6ZKGSdrTn30BpFiYesmy+Nu5eFK9ZlwQwnSRGcQ3/0EMW5Ske9+4TTak52LhNqRd975xW9KO0VeymuwkNgi8ltxV/v4jui7/lyoZ8o6GmFQy5B1dl/9Llb//SLpDy10h+78+FVKW+Ln7J5J+IGmdpFckrXT3l8xskZktim12jqQXzWyzolU8v+1RcfdNVaxAMoXholxS+BbBZc24jBKmi8wghsgFMWxRkvbnxa9K11f7QASVxAaB15K7rhr2aw23fT3ahts+XTXs12mKCJJC8399qqRyqKfc/TeSftOr7ZZuP/+zpH/u775Apuu93lLnRbmk7CuV3PnHMsXr4SAxNRtreqznJUltHW2q2ViT1N+xVFf3WzyntEeVSil7LzKD6r1M9bBFSRrScbh86IFJ3pCOw5N2jLOmjdUL7z2me9+4Tfvz3tOQjsN1zvjvZeXwuM6Yw1AJM0yvJQhH652E2oFMkNLED8g1QV2UByZLx7CHWRBrxgWxdECYLjLHFEX0F+8/or8f2nOuz/OHnZbu0BJ2zvjv6dd/vKHHcE/fn69zx38vaceo3V6rB3feJB/aJpPkQ9/TgztvUvn2I7Ly72QQCXlQwvRaUs36WPrEsnA6BMt4JC5b3zMSPyCJglrIGbmrOP8wNbXvjdueLEEVXgnLReaNf/66Jj//S0Viw75K7B39c/4v9eKfHyfp62mNLVE//foC6TH16I07d/z3klbYRQrhF2QByNaLzHQJ5P0KqJBIql9LYGuEhkg2v2ckfkASBbGQM3Jb1XvNqh7uahvy6RTtgv37VfVec9KOsbO5VWcO2XBAD9YDzScl7RhhMuMP/1fqNdcnYvui7fqb9AQ1CD/9+gL9VMlL9HrjC7LEZPNFZjoE9n4FMB0iiNcSZIXlIBLyII6RzVWpSfyQW+pXpvSPdNX0qh5z/KTkL+SMgQnLN+aVbzdIhRHVHF6kXUPzVPxJh6rea1ZlS/Lmk31nxLP6+/ZfdhUuKLFotboj8odJokfmAGFa+iQAfEGWmGy+yIwn1X+LA32/UjwdIojXEtQc5SCS2KCS/myuSp3K5RyAzFK/MjosY+8OSR69feCKaHuSBLGQMxIX2EK79SulGyZL1UXR2yT+bnUZWaLKlo+0vmGn6t/cofUNO1XZ8lFSl9n4+/xfxa1W9/f5v0raMUIlTEufBKBqepUK8gp6tPEFWd+y+SKztyD+Fofp/QritQRVYTmI6sdBHEPK7qrUJH7IHY9e03MsvhS9/+g1ST1M5YRKrT93veq/U6/1564n6csAgfxnEMAXC5ICWWZjeGv8IXd9tee8sC19kmJ8QZaYbL7I7C2Iv8Vher+CeC1BLeMRRBIbVNKfzUufkPghdzAcK2cF8p9BQF8sBLJALT1YiWHR4ITxBVn/ZfNFZm9B/C0O0/sVxGsJao3QIJLYoJL+oN6zVGCOH3JHH6WXuZgNvzFFETXGubBI6n8GQX6xkOplNgKqVheUQOZ3svQJUiRsS5+k+m9xmN6vs6aN1dgdD2rcxqX6nL+t3XaUdkxfrBnTTk/6cVL9/gSxdmuQ68Nma1VqEj/kjpBdzKL/AvnPIExfLARQrS4oVEREGGTrRWZvQV2Yh+X9Uv1KzdjyU0mtkknFelvFW34qHXd41v09DiIhD1PSnyrm7umOIWnKy8u9rq4u3WEgk6W4qicyV8p7fepXqva/F6vmsOGfVtt8/yNV/uVSfsfSaNZ1j8XtYRhbFNETV2bXGntAGISlwnIgbpjcxxeK46QfvRh8PMgaZva8u5f3bqfHD7mF4Vg5K9XfANeOKFT1kaPU5u2SpKb8oao+cpQ0opAFENIoTBX+gDAITW9cEKhNgCSjuAuA9AtiGYQUq9lY05X0dWrzdtVsrElTRJDCVeEPQI6h0BaSjMQPQJ/WbmrUrOse0/grazXruseSv+6dFNwyCCnW1BJ/qYO+2hGMMFX4A5BjWCoGScZQTyBLpXqeRGBFMQ62DEIWDcu1T4rkQ9+L2470YbI/gKwVokJbyAwkfkAWCiIpO9hCu0m9aN7boNrC4ao5vOjToijvNasyy+YwtP6pQoeMXi0b8ulwT9+fr7Y/VaQxKkjMKQKQxahNgCRiqCeQhQ6WlCVLUEUxao8qUfWRR6gpf6jcLFYU5QjVHpVdcxg+N+REtTXN0/59RXKX9u8rUlvTPH1uyInpDg0AAIAePyAbBZGUBbLouaSlhx2mtv0f9GhrGzJESw87NKuqYUbXp9qnlj9M62qL5Odp8TzmkgEAgPSjxw/IQkFUKgyqKMaejg8Sas9UZ00bq2vnTdHYoohM0XXirp03hSGGAAAgI9DjB2ShaO/Slh7DPZOdlAVVFGN/e5GGDGuO255tmEsGAAAyFYkfkIWCSsqCSGSGt5yh1qErDiiKMrzljJQeFwAAIJeQ+AFZKiy9S//PKRfqH9Z/IjviIVl+s7y9SP7uN/T/VFyY7tAAAABCg8QPQFpFk9fvaOm6L7POWj+leg1HAAAQPiR+ANIuLL2XQQhiDUcAABA+VPUEgCwSxBqOAAAgfEj8gGxVv1K6YbJUXRS9rV+Z7ogQgCDWcAQAAOFD4gdko/qV0gNXSHt3SPLo7QNXkPzlgCDWcAQAAOFD4gdko0evkdp79fC0t0bbEWqL55Qqkp/Xoy3ZazgCAIDwobgLkI32NiTWjtAIag1HAAAQLiR+QDYaWRIb5hmnHaFHFVQAAJAohnoC2Wj2Eim/15yu/Ei0HQAAAOiFxA9ItiCqbZbNl864SRo5TpJFb8+4KdoOAAAA9MJQTyCZOqttdhZe6ay2KSU/KSubT6IHAACAfqHHD0gmqm0CAAAgA5H4AclEtU0AAABkIBI/5JTa7bWqWFWhsjvLVLGqQrXba5N7gL6qalJtEwAAAGlE4oecUbu9VtVPVquppUkuV1NLk6qfrE5q8vfc5/9WrT6sR1urD9Nzn//bpB0DAAAASBSJH3JGzcYatXW09Whr62hTzcaapB3jhy9/QT9uv0QN+4/Ufjc17D9SP26/RD98+QtJOwYAAACQKKp6ImfsatmVUPtA7GxuVaNO0v37TurRbs2tfewBAAAApB49fsgYqZ5/V1xYnFD7QIwpiiTUDgAAAASBxA8ZIYj5d1XTq1SQV9CjrSCvQFXTq5J2jMVzShXJz+vRFsnP0+I5pUk7BgAAAJAoEj9khCDm31VOqFT1idUaXThaJtPowtGqPrFalRMqk3aMs6aN1bXzpmhsUUQmaWxRRNfOm6Kzpo1N2jEAAACARDHHDxkhiPl3UjT5S2aiF89Z08aS6AEAACCj0OOHjBDE/DsAAAAgV5H4ISMEMf8OAAAAyFUpTfzM7HQz22pm28zsyjiPX2hm9bF/T5rZl7o99qaZbTGzzWZWl8o4kX6VEypVXXK6Rne4zF2jO1zVJaenfFgmAAAAkAtSNsfPzPIk3SzpNEkNkp4zs/vd/eVum70h6RR3f8/MviHpVkkndHv8VHd/J1Uxon/WbmrU0nVbtbO5VWOKIlo8pzT5c9jqV6ryidtU2d5tvbum26Qjpkhl85N7LAAAACDHpLLHb6akbe6+3d33SVohaW73Ddz9SXd/L3b3aUklKYwHA7B2U6OuWr1Fjc2tckmNza26avUWrd3UmNwDPXqN1N5rkfP21mg7AAAAgEFJZeI3VtKObvcbYm19+WtJD3W775LWm9nzZnZpCuJDPyxdt1Wt7R092lrbO7R03dbkHmhvQ2LtAAAAAPotlcs5WJw2j7uh2amKJn4ndWue5e47zexzkh4xs1fd/X/i7HuppEsl6Zhjjhl81OhhZ3NrQu0DNrJE2rsjfjsAAACAQUllj1+DpHHd7pdI2tl7IzMrk/RLSXPdfU9nu7vvjN3ulrRG0aGjB3D3W9293N3LjzrqqCSGD0kaUxRJqH3AZi+R8ns9Z34k2g4AAABgUD4z8TOzvzKzgSSIz0n6gpmNN7Nhks6TdH+v5z5G0mpJC9z9tW7thWZ2aOfPkiokvTiAGDBIi+eUKpKf16Mtkp+nxXNKk3ugsvnSGTdJI8dJsujtGTdR2AUAAABIgv4M9TxPUo2Z3SvpDnd/pT9P7O6fmNkPJK2TlCfpdnd/ycwWxR6/RdISSaMk/cLMJOkTdy+XdLSkNbG2oZL+y90fTuylIRnOmjZWL7z3mO594zbtz3tPQzoO1znjv5f8qp6S1nbM0tKPb9LOtlaNKYhocUepzkr6UQAAAIDcY+5xp9313MjsMEnnS7pY0Xl6d0i6x90/SG14iSkvL/e6Opb8S6ba7bWqfrJabR1tXW0FeQWqPrE6qWvsdVYP7V5IJpKfp2vnTUlJkgkAAACEkZk9H+tM66FfQzjd/X1J9yq6JMNoSWdL2mhmf5vUKJFxajbW9Ej6JKmto001G2uSepzAqocCAAAAOag/c/zOMLM1kh6TlC9pprt/Q9KXJP1diuNDmu1q2ZVQ+0AFVj0UAAAAyEH9meP3LUk39F5Kwd0/MrPvpiYsZIriwmI1tTTFbU+mMUURNcZJ8pJePRQAAADIQf0Z6vlTSc923jGziJkdJ0nu/miK4kKGqJpepYK8gh5tBXkFqppeldTjBFY9FAAAAMhB/enx+7WkE7vd74i1zUhJRMgonQVcajbWaFfLLhUXFqtqelVSC7tI6irgsnTdVu1sbtWYoogWzymlsAsAAACQBJ9Z1dPMNrv71F5tL7j7l1IZ2EBQ1RMAAABALhtMVc+3zezMbk80V9I7yQwOAAAAAJA6/RnquUjS3Wa2TJJJ2iHpopRGBQAAAABIms9M/Nz9D5K+bGYjFB0amlGLtgMAAAAADq4/PX4ys0pJx0sqMDNJkrtfk8K4AAAAAABJ0p8F3G+R9G1Jf6voUM9vSTo2xXEBAAAAAJKkP8VdTnT3iyS95+5XS/qKpHGpDQsAAAAAkCz9SfzaYrcfmdkYSe2SxqcuJAAAAABAMvUn8XvAzIokLZW0UdKbku5JYUwA+qF2e60qVlWo7M4yVayqUO322nSHBAAAgAx10OIuZjZE0qPu3izpXjN7UFKBu+8NIjgA8dVur1X1k9Vq64h2yDe1NKn6yWpJUuWEyjRGBgAAgEx00B4/d98v6Wfd7n9M0gekX83Gmq6kr1NbR5tqNtakKSIAAABksv4M9VxvZudY5zoOANJuV8uuhNoBAACQ2/qzjt//llQo6RMza1N0SQd398NSGhmAPhUXFquppSluOwAAANDbZ/b4ufuh7j7E3Ye5+2Gx+yR9QBpVTa9SQV5Bj7aCvAJVTa9KU0QAAADIZJ/Z42dmJ8drd/f/SX44APqjs4BLzcYa7WrZpeLCYlVNr6KwCwAAAOLqz1DPxd1+LpA0U9Lzkr6ekogA9EvlhEoSPQAAAPTLZyZ+7n5G9/tmNk7Sv6QsImSe+pXSo9dIexukkSXS7CVS2fx0RwUAAACgn/rT49dbg6TJyQ4EGap+pfTAFVJ7a/T+3h3R+xLJHwAAAJAl+jPH7/9K8tjdIZKmSnohhTEhkzx6zadJX6f21mg7iR8AAACQFfrT41fX7edPJN3j7k+kKB5kmr0NibUDAAAAyDj9SfxWSWpz9w5JMrM8Mxvu7h+lNjRkhJEl0eGd8doBAAAAZIXPXMdP0qOSIt3uRyT9d2rCQcaZvUTKj/Rsy49E2wEAAABkhf4kfgXu/mHnndjPw1MXEjJK2XzpjJukkeMkWfT2jJuY3wcAAABkkf4M9Wwxs+nuvlGSzOwvJLV+xj4Ik7L5JHoAAABAFutP4vdDSb82s52x+6MlfTtlEQEAAAAAkqo/C7g/Z2aTJJVKMkmvunt7yiMDAAAAACRFf9bxu1zS3e7+Yuz+4WZ2vrv/IuXRAUm2dlOjlq7bqp3NrRpTFNHiOaU6a9rYdIcFAAAApFR/irt8z92bO++4+3uSvpeyiIAUWbupUVet3qLG5la5pMbmVl21eovWbmpMd2gAAABASvUn8RtiZtZ5x8zyJA1LXUhAaixdt1Wt7R092lrbO7R03dY0RQQAAAAEoz/FXdZJWmlmt0hySYskPZTSqIAU2NkcvxhtX+0AAABAWPSnx+/Hii7ifpmkyyXVq+eC7kBWGFMU/9e2r3YAAAAgLD4z8XP3/ZKelrRdUrmk2ZJeSXFcQNItnlOqSH5ej7ZIfp4WzylNU0QAAABAMPoc6mlmfybpPEnnS9oj6VeS5O6nBhMakFyd1Tup6gkAAIBcc7A5fq9K+r2kM9x9mySZ2Y8CiQpIkbOmjSXRAwAAQM452FDPcyTtkvRbM7vNzGYruoA7AAAAACCL9Jn4ufsad/+2pEmSHpf0I0lHm9m/mllFQPEBAAAAAAapP8VdWtz9bnf/K0klkjZLujLVgaF/arfXqmJVhcruLFPFqgrVbq9Nd0gAAAAAMkx/1vHr4u7vSvq32D+kWe32WlU/Wa22jjZJUlNLk6qfrJYkVU6oTGNkAAAAADJJf9bxQ4aq2VjTlfR1autoU83GmjRFBAAAACATpTTxM7PTzWyrmW0zswOGh5rZhWZWH/v3pJl9qb/7QtrVsiuhdgAAAAC5KWWJn5nlSbpZ0jck/bmk883sz3tt9oakU9y9TNI/Sro1gX1zXnFhcULtAAAAAHJTKnv8Zkra5u7b3X2fpBWS5nbfwN2fdPf3YnefVrR4TL/2hVQ1vUoFeQU92gryClQ1vSpNEQEAAADIRAkVd0nQWEk7ut1vkHTCQbb/a0kPDXDfnNRZwKVmY412texScWGxqqZXUdgFAAAAQA+pTPziLfbucTc0O1XRxO+kAex7qaRLJemYY45JPMosVzmhkkQPAAAAwEGlcqhng6Rx3e6XSNrZeyMzK5P0S0lz3X1PIvtKkrvf6u7l7l5+1FFHJSVwAAAAAAiTVCZ+z0n6gpmNN7Nhks6TdH/3DczsGEmrJS1w99cS2RcAAAAA0D8pG+rp7p+Y2Q8krZOUJ+l2d3/JzBbFHr9F0hJJoyT9wswk6ZNY713cfVMVKwAAAACEmbnHnTqXlcrLy72uri7dYQAAAABAWpjZ8+5e3rs9pQu4AwAAAADSj8QPAAAAAEKOxA8AAAAAQo7EDwAAAABCjsQPAAAAAEKOxA8AAAAAQi5l6/ghGGs3NWrpuq3a2dyqMUURLZ5TqrOmjU13WAAAAAAyCIlfFlu7qVFXrd6i1vYOSVJjc6uuWr1Fkkj+AAAAAHRhqGcWW7pua1fS16m1vUNL121NU0QAAAAAMhGJXxbb2dyaUDsAAACA3ETil8XGFEUSagcAAACQm0j8stjiOaWK5Of1aIvk52nxnNI0RQQAAAAgE1HcJYt1FnChqicAAACAgyHxy3JnTRtLogcAAADgoBjqCQAAAAAhR+IHAAAAACFH4pft6ldKN0yWqouit/Ur0x0RAAAAgAzDHL9sVr9SeuAKqT22bt/eHdH7klQ2P31xAQAAAMgo9Phls0ev+TTp69TeGm0HAAAAgBgSv2y2tyGxdgAAAAA5icQvm40sSawdAAAAQE4i8ctms5dI+ZGebfmRaDsAAAAAxJD4ZbOy+dIZN0kjx0my6O0ZN1HYBQAAAEAPVPXMdmXzSfQAAAAAHBQ9fgAAAAAQciR+AAAAABByJH4AAAAAEHIkfgAAAAAQciR+AAAAABByJH4AAAAAEHIkfgAAAAAQciR+AAAAABByJH4AAAAAEHIkfqlUv1K6YbJUXRS9rV+Z7ogAAAAA5KCh6Q4gtOpXSg9cIbW3Ru/v3RG9L0ll89MXFwAAAICcQ49fqjx6zadJX6f21mg7AAAAAASIxC9FfG9DQu0AAAAAkCokfinyJx2ZUDsAAAAApAqJX4pcu+9b+siH9Wj7yIfp2n3fSlNEAAAAAHIViV+K1B12mq5sv0QN+4/Ufjc17D9SV7ZforrDTkt3aAAAAAByDFU9U2TxnFJdtXqf7t93UldbJD9P184pTWNUAAAAAHIRiV+KnDVtrCRp6bqt2tncqjFFES2eU9rVDgAAAABBIfFLobOmjSXRAwAAAJB2KZ3jZ2anm9lWM9tmZlfGeXySmT1lZh+b2d/1euxNM9tiZpvNrC6VcQIAAABAmKWsx8/M8iTdLOk0SQ2SnjOz+9395W6bvSvpCkln9fE0p7r7O6mKEQAAAAByQSp7/GZK2ubu2919n6QVkuZ238Ddd7v7c5LaUxgHAAAAAOS0VCZ+YyXt6Ha/IdbWXy5pvZk9b2aXJjUyAAAAAMghqSzuYnHaPIH9Z7n7TjP7nKRHzOxVd/+fAw4STQovlaRjjjlmYJECAAAg5drb29XQ0KC2trZ0hwJkvYKCApWUlCg/P79f26cy8WuQNK7b/RJJO/u7s7vvjN3uNrM1ig4dPSDxc/dbJd0qSeXl5YkklgAAAAhQQ0ODDj30UB133HEyi9dHAKA/3F179uxRQ0ODxo8f3699UjnU8zlJXzCz8WY2TNJ5ku7vz45mVmhmh3b+LKlC0ospixQAAAAp19bWplGjRpH0AYNkZho1alRCvecp6/Fz90/M7AeS1knKk3S7u79kZotij99iZsWS6iQdJmm/mf1Q0p9LOlLSmtgfhaGS/svdH05VrAAAAAgGSR+QHIl+llK6jp+7/8bd/8zdP+/u/xRru8Xdb4n9vMvdS9z9MHcviv38fqwS6Jdi/47v3BcAAAAYjIcfflilpaWaOHGirrvuurjbuLuuuOIKTZw4UWVlZdq4cWNC+/c2YsSIpMQ+EMuXL9fOnf2ebZXx+vP+L126VFOnTtXUqVM1efJk5eXl6d133+33/r2F5fylNPEDAAAABmrtpkbNuu4xjb+yVrOue0xrNzUO6vk6Ojp0+eWX66GHHtLLL7+se+65Ry+//PIB2z300EN6/fXX9frrr+vWW2/VZZddltD+/Y0lCOlM/Gq316piVYXK7ixTxaoK1W6vHdTz9ff9X7x4sTZv3qzNmzfr2muv1SmnnKIjjjgi588fiR8AAAAyztpNjbpq9RY1NrfKJTU2t+qq1VsGlfw9++yzmjhxoiZMmKBhw4bpvPPO03333XfAdvfdd58uuugimZm+/OUvq7m5WU1NTf3e/4033tBXvvIVzZgxQz/5yU+62h9//HGdeuqpuuCCCzRlyhS1tbXp4osv1pQpUzRt2jT99re/lRS92J87d65OP/10lZaW6uqrr+56jp///OeaPHmyJk+erBtvvFGS9Oabb2ry5Mld21x//fWqrq7WqlWrVFdXpwsvvFBTp05Va2vrgN+7RNVur1X1k9VqammSy9XU0qTqJ6sHlfz19/3v7p577tH555+f0P5hPX+prOoJAAAADMjSdVvV2t6zV6W1vUNL123VWdMSWRr6U42NjRo37tOi8yUlJXrmmWf6tV1jY2O/96+qqtJll12miy66SDfffHOPx5599lm9+OKLGj9+vH72s59JkrZs2aJXX31VFRUVeu2113psN3z4cM2YMUOVlZUyM91xxx165pln5O464YQTdMopp+jwww+P+3rPPfdcLVu2TNdff73Ky8sTeKcGr2Zjjdo6ehYeaetoU83GGlVOqBzQc/b3/e/00Ucf6eGHH9ayZcsS2j+s548ePwAAAGScnc3xezf6au8P9wNX/opXIKOv7fq7/xNPPNHVy7RgwYIej82cObOr/P6GDRu6Hp80aZKOPfbYrsThtNNO06hRoxSJRDRv3jxt2LBBGzZs0Nlnn63CwkKNGDFC8+bN0+9///vPetlpsatlV0Lt/dHf97/TAw88oFmzZumII45IaP+wnj8SPwAAAGScMUWRhNr7o6SkRDt27Oi639DQoDFjxvR7u/7uL/WdkBQWFnb9HC8R6Wv/vhJPSRo6dKj279/fdT+REv+pUlxYnFB7fyTy/kvSihUruhK4RPcP4/kj8QMAAEDGWTynVJH8vB5tkfw8LZ5TOuDnnDFjhl5//XW98cYb2rdvn1asWKEzzzzzgO3OPPNM3XXXXXJ3Pf300xo5cqRGjx7d7/1nzZqlFStWSJLuvvvuPuM5+eSTux5/7bXX9NZbb6m0NPr6HnnkEb377rtqbW3V2rVrNWvWLJ188slau3atPvroI7W0tGjNmjX66le/qqOPPlq7d+/Wnj179PHHH+vBBx/sOsahhx6qDz74YMDv2UBVTa9SQV5Bj7aCvAJVTa8a8HP29/2XpL179+p3v/ud5s6dm/D+YT1/zPEDAABAxumcx7d03VbtbG7VmKKIFs8pHfD8Pinas7Js2TLNmTNHHR0d+u53v6vjjz9eknTLLbdIkhYtWqRvfvOb+s1vfqOJEydq+PDhuuOOOz5z/+5qamp0wQUXqKamRuecc06f8Xz/+9/XokWLNGXKFA0dOlTLly/XIYccIkk66aSTtGDBAm3btk0XXHBB1xyvhQsXaubMmZKkSy65RNOmTZMkLVmyRCeccILGjx+vSZMmdR1j4cKFWrRokSKRiJ566ilFIgPvMU1E5zy+mo012tWyS8WFxaqaXjXg+X1S/8+fJK1Zs0YVFRU9euhy/fzZwboos015ebnX1dWlOwwAAADE8corr+iLX/xiusPIeMuXL1ddXV1XURJklyDPX7zPlJk97+4HVINhqCcAAAAAhBw9fgAAAAgEPX5ActHjBwAAAADoQuIHAAAAACFH4gcAAAAAIUfiBwAAAAAhR+IHAACAnPHwww+rtLRUEydO1HXXXRd3m/vuu09lZWWaOnWqysvLtWHDhoT2723EiBFJiX0gli9frp07d6bt+MgcJH4AAADITPUrpRsmS9VF0dv6lYN6uo6ODl1++eV66KGH9PLLL+uee+7Ryy+/fMB2s2fP1gsvvKDNmzfr9ttv1yWXXJLQ/v2NJQgkfuhE4gcAAIDMU79SeuAKae8OSR69feCKQSV/zz77rCZOnKgJEyZo2LBhOu+883TfffcdsN2IESNkZpKklpaWrp/7u/8bb7yhr3zlK5oxY4Z+8pOfdLU//vjjOvXUU3XBBRdoypQpamtr08UXX6wpU6Zo2rRp+u1vfyspmqzNnTtXp59+ukpLS3X11Vd3PcfPf/5zTZ48WZMnT9aNN94oSXrzzTc1efLkrm2uv/56VVdXa9WqVaqrq9OFF16oqVOnqrW1dcDvHbLf0HQHAAAAABzg0Wuk9l6JSntrtL1s/oCesrGxUePGjeu6X1JSomeeeSbutmvWrNFVV12l3bt3q7a2NqH9q6qqdNlll+miiy7SzTff3OOxZ599Vi+++KLGjx+vn/3sZ5KkLVu26NVXX1VFRYVee+21HtsNHz5cM2bMUGVlpcxMd9xxh5555hm5u0444QSdcsopOvzww+O+hnPPPVfLli3T9ddfr/LyA5Z1Q46hxw8AAACZZ29DYu394O4HtHX25vV29tln69VXX9XatWu7eu36u/8TTzyh888/X5K0YMGCHo/NnDlT48ePlyRt2LCh6/FJkybp2GOP7Ur8TjvtNI0aNUqRSETz5s3Thg0btGHDBp199tkqLCzUiBEjNG/ePP3+97/v78tHjiPxS6Ha7bWqWFWhsjvLVLGqQrXba9MdEgAAQHYYWZJYez+UlJRox44dXfcbGho0ZsyYg+5z8skn6w9/+IPeeeedhPbvK6EsLCzs+jleItnX/mbW5/ZDhw7V/v37u+63tbX1+bzIXSR+KVK7vVbVT1arqaVJLldTS5Oqn6wm+QMAAOiP2Uuk/EjPtvxItH2AZsyYoddff11vvPGG9u3bpxUrVujMM888YLtt27Z1JVkbN27Uvn37NGrUqH7vP2vWLK1YsUKSdPfdd/cZz8knn9z1+Guvvaa33npLpaWlkqRHHnlE7777rlpbW7V27VrNmjVLJ598stauXauPPvpILS0tWrNmjb761a/q6KOP1u7du7Vnzx59/PHHevDBB7uOceihh+qDDz4Y8HuG8GCOX4rUbKxRW0fPb1vaOtpUs7FGlRMq0xQVAABAluicx/foNdHhnSNLoknfAOf3SdGesWXLlmnOnDnq6OjQd7/7XR1//PGSpFtuuUWStGjRIt1777266667lJ+fr0gkol/96lcys4Pu311NTY0uuOAC1dTU6Jxzzukznu9///tatGiRpkyZoqFDh2r58uU65JBDJEknnXSSFixYoG3btumCCy7omqO3cOFCzZw5U5J0ySWXaNq0aZKkJUuW6IQTTtD48eM1adKkrmMsXLhQixYtUiQS0VNPPaVIpFcyjZxhB+tizjbl5eVeV1eX7jAkSWV3lskVZxy4TPXfqU9DRAAAAOn1yiuv6Itf/GK6w8h4y5cvV11dnZYtW5buUJDh4n2mzOx5dz+gmg9DPVOkuLA4oXYAAAAASBUSvxSpml6lgryCHm0FeQWqml6VpogAAACQDRYuXEhvH5KOOX4p0jmPr2ZjjXa17FJxYbGqplcxvw8AAABA4Ej8UqhyQiWJHgAAQDfu3udSBwD6L9FaLQz1BAAAQCAKCgq0Z8+ehC9YAfTk7tqzZ48KCgo+e+MYevwAAAAQiJKSEjU0NOjtt99OdyhA1isoKFBJSUm/tyfxAwAAQCDy8/M1fvz4dIcB5CSGegIAAABAyJH4AQAAAEDIkfgBAAAAQMhZmKoqmdnbkv6Y7jjiOFLSO+kOAoHjvOcuzn3u4tznLs597uLc565MPffHuvtRvRtDlfhlKjOrc/fydMeBYHHecxfnPndx7nMX5z53ce5zV7ade4Z6AgAAAEDIkfgBAAAAQMiR+AXj1nQHgLTgvOcuzn3u4tznLs597uLc566sOvfM8QMAAACAkKPHDwAAAABCjsQvhczsdDPbambbzOzKdMeD4JjZm2a2xcw2m1lduuNB6pjZ7Wa228xe7NZ2hJk9Ymavx24PT2eMSI0+zn21mTXGPvubzeyb6YwRyWdm48zst2b2ipm9ZGZVsXY+9yF3kHPP5z7kzKzAzJ41sxdi5/7qWHtWfe4Z6pkiZpYn6TVJp0lqkPScpPPd/eW0BoZAmNmbksrdPRPXdkESmdnJkj6UdJe7T461/Yukd939utiXPoe7+4/TGSeSr49zXy3pQ3e/Pp2xIXXMbLSk0e6+0cwOlfS8pLMkLRSf+1A7yLmfLz73oWZmJqnQ3T80s3xJGyRVSZqnLPrc0+OXOjMlbXP37e6+T9IKSXPTHBOAJHP3/5H0bq/muZLujP18p6IXBgiZPs49Qs7dm9x9Y+znDyS9Imms+NyH3kHOPULOoz6M3c2P/XNl2eeexC91xkra0e1+g/jjkEtc0noze97MLk13MAjc0e7eJEUvFCR9Ls3xIFg/MLP62FDQjB72g8Exs+MkTZP0jPjc55Re517icx96ZpZnZpsl7Zb0iLtn3eeexC91LE4b42pzxyx3ny7pG5Iujw0JAxB+/yrp85KmSmqS9LO0RoOUMbMRku6V9EN3fz/d8SA4cc49n/sc4O4d7j5VUomkmWY2Oc0hJYzEL3UaJI3rdr9E0s40xYKAufvO2O1uSWsUHfqL3PGn2FyQzjkhu9McDwLi7n+KXRzsl3Sb+OyHUmyOz72S7nb31bFmPvc5IN6553OfW9y9WdLjkk5Xln3uSfxS5zlJXzCz8WY2TNJ5ku5Pc0wIgJkVxiZ9y8wKJVVIevHgeyFk7pf0ndjP35F0XxpjQYA6LwBizhaf/dCJFXn4d0mvuPvPuz3E5z7k+jr3fO7Dz8yOMrOi2M8RSX8p6VVl2eeeqp4pFCvne6OkPEm3u/s/pTciBMHMJijayydJQyX9F+c+vMzsHklfk3SkpD9J+qmktZJWSjpG0luSvuXuFAEJmT7O/dcUHe7lkt6U9Ded8z8QDmZ2kqTfS9oiaX+s+R8UnevF5z7EDnLuzxef+1AzszJFi7fkKdpxttLdrzGzUcqizz2JHwAAAACEHEM9AQAAACDkSPwAAAAAIORI/AAAAAAg5Ej8AAAAACDkSPwAAAAAIORI/AAA6MXMOsxsc7d/VybxuY8zM9b5AgAEami6AwAAIAO1uvvUdAcBAECy0OMHAEA/mdmbZvbPZvZs7N/EWPuxZvaomdXHbo+JtR9tZmvM7IXYvxNjT5VnZreZ2Utmtt7MIml7UQCAnEDiBwDAgSK9hnp+u9tj77v7TEnLJN0Ya1sm6S53L5N0t6SbYu03Sfqdu39J0nRJL8XavyDpZnc/XlKzpHNS+moAADnP3D3dMQAAkFHM7EN3HxGn/U1JX3f37WaWL2mXu48ys3ckjXb39lh7k7sfaWZvSypx94+7Pcdxkh5x9y/E7v9YUr67/78BvDQAQI6ixw8AgMR4Hz/3tU08H3f7uUPMuQcApBiJHwAAifl2t9unYj8/Kem82M8XStoQ+/lRSZdJkpnlmdlhQQUJAEB3fMMIAMCBIma2udv9h929c0mHQ8zsGUW/PD0/1naFpNvNbLGktyVdHGuvknSrmf21oj17l0lqSnXwAAD0xhw/AAD6KTbHr9zd30l3LAAAJIKhngAAAAAQcvT4AQAAAEDI0eMHAAAAACFH4gcAAAAAIUfiBwAAAAAhR+IHAAAAACFH4gcAAAAAIUfiBwAAAAAh9/8DIjzcFsroe4gAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x1080 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_accs = []\n",
    "val_accs = []\n",
    "for dropout in dropout_choices:\n",
    "    solver = solvers[dropout]\n",
    "    train_accs.append(solver.train_acc_history[-1])\n",
    "    val_accs.append(solver.val_acc_history[-1])\n",
    "\n",
    "plt.subplot(3, 1, 1)\n",
    "for dropout in dropout_choices:\n",
    "    plt.plot(solvers[dropout].train_acc_history, 'o', label='%.2f dropout' % dropout)\n",
    "plt.title('Train accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.legend(ncol=2, loc='lower right')\n",
    "    \n",
    "plt.subplot(3, 1, 2)\n",
    "for dropout in dropout_choices:\n",
    "    plt.plot(solvers[dropout].val_acc_history, 'o', label='%.2f dropout' % dropout)\n",
    "plt.title('Val accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.legend(ncol=2, loc='lower right')\n",
    "\n",
    "plt.gcf().set_size_inches(15, 15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "5a831fb0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(迭代 1 / 4900) 损失值: 2.319642\n",
      "(周期 0 / 50) 训练精度: 0.104000; 验证精度: 0.092000\n",
      "(周期 1 / 50) 训练精度: 0.210000; 验证精度: 0.228000\n",
      "(周期 2 / 50) 训练精度: 0.272000; 验证精度: 0.268000\n",
      "(周期 3 / 50) 训练精度: 0.288000; 验证精度: 0.296000\n",
      "(迭代 301 / 4900) 损失值: 2.029446\n",
      "(周期 4 / 50) 训练精度: 0.322000; 验证精度: 0.316000\n",
      "(周期 5 / 50) 训练精度: 0.308000; 验证精度: 0.332000\n",
      "(周期 6 / 50) 训练精度: 0.317000; 验证精度: 0.342000\n",
      "(迭代 601 / 4900) 损失值: 1.949471\n",
      "(周期 7 / 50) 训练精度: 0.352000; 验证精度: 0.355000\n",
      "(周期 8 / 50) 训练精度: 0.344000; 验证精度: 0.362000\n",
      "(周期 9 / 50) 训练精度: 0.353000; 验证精度: 0.373000\n",
      "(迭代 901 / 4900) 损失值: 1.940363\n",
      "(周期 10 / 50) 训练精度: 0.363000; 验证精度: 0.374000\n",
      "(周期 11 / 50) 训练精度: 0.354000; 验证精度: 0.374000\n",
      "(周期 12 / 50) 训练精度: 0.361000; 验证精度: 0.377000\n",
      "(迭代 1201 / 4900) 损失值: 1.855814\n",
      "(周期 13 / 50) 训练精度: 0.380000; 验证精度: 0.376000\n",
      "(周期 14 / 50) 训练精度: 0.381000; 验证精度: 0.387000\n",
      "(周期 15 / 50) 训练精度: 0.356000; 验证精度: 0.384000\n",
      "(迭代 1501 / 4900) 损失值: 1.775489\n",
      "(周期 16 / 50) 训练精度: 0.382000; 验证精度: 0.390000\n",
      "(周期 17 / 50) 训练精度: 0.409000; 验证精度: 0.389000\n",
      "(周期 18 / 50) 训练精度: 0.368000; 验证精度: 0.397000\n",
      "(迭代 1801 / 4900) 损失值: 1.882715\n",
      "(周期 19 / 50) 训练精度: 0.403000; 验证精度: 0.397000\n",
      "(周期 20 / 50) 训练精度: 0.396000; 验证精度: 0.400000\n",
      "(周期 21 / 50) 训练精度: 0.402000; 验证精度: 0.402000\n",
      "(迭代 2101 / 4900) 损失值: 1.816344\n",
      "(周期 22 / 50) 训练精度: 0.398000; 验证精度: 0.403000\n",
      "(周期 23 / 50) 训练精度: 0.400000; 验证精度: 0.402000\n",
      "(周期 24 / 50) 训练精度: 0.403000; 验证精度: 0.409000\n",
      "(迭代 2401 / 4900) 损失值: 1.773112\n",
      "(周期 25 / 50) 训练精度: 0.414000; 验证精度: 0.413000\n",
      "(周期 26 / 50) 训练精度: 0.375000; 验证精度: 0.409000\n",
      "(周期 27 / 50) 训练精度: 0.391000; 验证精度: 0.408000\n",
      "(迭代 2701 / 4900) 损失值: 1.856824\n",
      "(周期 28 / 50) 训练精度: 0.416000; 验证精度: 0.416000\n",
      "(周期 29 / 50) 训练精度: 0.388000; 验证精度: 0.417000\n",
      "(周期 30 / 50) 训练精度: 0.437000; 验证精度: 0.416000\n",
      "(迭代 3001 / 4900) 损失值: 1.766612\n",
      "(周期 31 / 50) 训练精度: 0.380000; 验证精度: 0.422000\n",
      "(周期 32 / 50) 训练精度: 0.410000; 验证精度: 0.420000\n",
      "(周期 33 / 50) 训练精度: 0.390000; 验证精度: 0.416000\n",
      "(迭代 3301 / 4900) 损失值: 1.738727\n",
      "(周期 34 / 50) 训练精度: 0.398000; 验证精度: 0.421000\n",
      "(周期 35 / 50) 训练精度: 0.414000; 验证精度: 0.419000\n",
      "(周期 36 / 50) 训练精度: 0.396000; 验证精度: 0.422000\n",
      "(迭代 3601 / 4900) 损失值: 1.751838\n",
      "(周期 37 / 50) 训练精度: 0.418000; 验证精度: 0.425000\n",
      "(周期 38 / 50) 训练精度: 0.386000; 验证精度: 0.422000\n",
      "(周期 39 / 50) 训练精度: 0.417000; 验证精度: 0.423000\n",
      "(迭代 3901 / 4900) 损失值: 1.732294\n",
      "(周期 40 / 50) 训练精度: 0.443000; 验证精度: 0.421000\n",
      "(周期 41 / 50) 训练精度: 0.381000; 验证精度: 0.422000\n",
      "(周期 42 / 50) 训练精度: 0.404000; 验证精度: 0.420000\n",
      "(迭代 4201 / 4900) 损失值: 1.695655\n",
      "(周期 43 / 50) 训练精度: 0.414000; 验证精度: 0.422000\n",
      "(周期 44 / 50) 训练精度: 0.409000; 验证精度: 0.422000\n",
      "(周期 45 / 50) 训练精度: 0.425000; 验证精度: 0.424000\n",
      "(迭代 4501 / 4900) 损失值: 1.739231\n",
      "(周期 46 / 50) 训练精度: 0.433000; 验证精度: 0.422000\n",
      "(周期 47 / 50) 训练精度: 0.417000; 验证精度: 0.423000\n",
      "(周期 48 / 50) 训练精度: 0.399000; 验证精度: 0.421000\n",
      "(迭代 4801 / 4900) 损失值: 1.758454\n",
      "(周期 49 / 50) 训练精度: 0.390000; 验证精度: 0.423000\n",
      "(周期 50 / 50) 训练精度: 0.391000; 验证精度: 0.419000\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = None\n",
    "trainer = None\n",
    "\n",
    "D,C,std,r= 3*32*32,10,1e-2,0.6\n",
    "model = FullyConnectedNet(input_dim=D, hidden_dims=[100,50], num_classes=C, weight_scale=std, dropout=0.7)\n",
    "trainer = Trainer(model,data,update_rule='sgd',\n",
    "                updater_config={'learning_rate': 1e-3,},\n",
    "                lr_decay=0.95,num_epochs=50, batch_size=500,print_every=300)\n",
    "trainer.train()\n",
    "# 可视化训练/验证结果\n",
    "plt.subplot(2, 1, 1)\n",
    "plt.title('Training loss')\n",
    "plt.plot(trainer.loss_history, 'o')\n",
    "plt.xlabel('Iteration')\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.title('Accuracy')\n",
    "plt.plot(trainer.train_acc_history, '-o', label='train')\n",
    "plt.plot(trainer.val_acc_history, '-o', label='val')\n",
    "plt.plot([0.5] * len(trainer.val_acc_history), 'k--')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(loc='lower right')\n",
    "plt.gcf().set_size_inches(15, 12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "13610e1d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
